Author: "Xerox Company" / Topic: computer - Searchworks@Jio Institute Digital Library Search Results

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38 results on '"Xerox Company"'

1. Label-Embedding for Image Classification

Author: Zeynep Akata, Zaid Harchaoui, Florent Perronnin, Cordelia Schmid, Xerox Research Centre Europe [Meylan], Xerox Company, Apprentissage de modèles à partir de données massives (Thoth ), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire Jean Kuntzmann (LJK ), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)
Subjects: FOS: Computer and information sciences, Label Embedding, Linear programming, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Text mining, Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 0105 earth and related environmental sciences, Training set, Image Classification, Contextual image classification, business.industry, Applied Mathematics, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Computational Theory and Mathematics, Embedding, Zero-Shot Learning, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, Attributes, business, computer, Software
Abstract: Attributes act as intermediate representations that enable parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function that measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. Label embedding enjoys a built-in ability to leverage alternative sources of information instead of or in addition to attributes, such as e.g. class hierarchies or textual descriptions. Moreover, label embedding encompasses the whole range of learning settings from zero-shot learning to regular learning with a large number of labeled examples., Comment: IEEE TPAMI preprint
Published: 2016
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2. BEST INDIVIDUAL TEMPLATE SELECTION FROM DEFORMATION TENSOR MINIMIZATION

Author: C. Brun, Arthur W. Toga, M. Meredith, Marina Barysheva, Margaret J. Wright, Katie L. McMahon, Yi-Yu Chou, Xavier Pennec, Paul M. Thompson, G.I. de Zubicaray, Natasha Lepore, Agatha D. Lee, Laboratory of Neuro Imaging [Los Angeles] (LONI), University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Xerox Research Centre Europe [Meylan], Xerox Company, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Centre for Magnetic Resonance [Brisbanne], University of Queensland [Brisbane], Queensland Institute of Medical Research, and University of California-University of California
Subjects: education.field_of_study, Similarity (geometry), business.industry, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Cumulative distribution function, Population, Monozygotic twin, Pattern recognition, computer.software_genre, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Article, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Voxel, Metric (mathematics), Null distribution, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Computer vision, Tensor, Artificial intelligence, business, education, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, Mathematics
Abstract: We study the influence of the choice of template in tensor- based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on average than random pairings, consistent with evidence that brain structure is under strong genetic control. We also computed the intraclass correlation and associated permutation p-value at each voxel for the determinant of the Jacobian matrix of the transformation. The cumulative distribution function (cdf) of the p-values was found at each voxel for each of the templates and compared to the null distribution. Surprisingly, there was very little difference between CDFs of statistics computed from analyses using different templates. As the brain with least log-Euclidean deformation cost, the mean template defined here avoids the blurring caused by creating a synthetic image from a population, and when selected from a large population, avoids bias by being geometrically centered, in a metric that is sensitive enough to anatomical similarity that it can even detect genetic affinity among anatomies.
Published: 2018
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3. Symbolic Priors for RNN-based Semantic Parsing

Author: Marc Dymetman, Chunyang Xiao, Claire Gardent, Xerox Research Centre Europe [Meylan], Xerox Company, Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Parsing, Computer Science - Computation and Language, Grammar, Intersection (set theory), Computer science, business.industry, media_common.quotation_subject, 020207 software engineering, 02 engineering and technology, computer.software_genre, Automaton, Recurrent neural network, 0202 electrical engineering, electronic engineering, information engineering, Logical form, 020201 artificial intelligence & image processing, S-attributed grammar, [INFO]Computer Science [cs], Artificial intelligence, business, computer, Computation and Language (cs.CL), Natural language processing, Natural language, media_common
Abstract: Seq2seq models based on Recurrent Neural Networks (RNNs) have recently received a lot of attention in the domain of Semantic Parsing. While in principle they can be trained directly on pairs (natural language utterances, logical forms), their performance is limited by the amount of available data. To alleviate this problem, we propose to exploit various sources of prior knowledge: the well-formedness of the logical forms is modeled by a weighted context-free grammar; the likelihood that certain entities present in the input utterance are also present in the logical form is modeled by weighted finite-state automata. The grammar and automata are combined together through an efficient intersection algorithm to form a soft guide (“background”) to the RNN.We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features.
Published: 2018
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4. Accuracy of using natural language processing methods for identifying healthcare-associated infections

Author: Stéfan Jacques Darmoni, Frédérique Segond, Nastassia Tvardik, Ivan Kergourlay, M.-H. Metzger, André Bittar, Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Université Le Havre Normandie (ULH), Normandie Université (NU), Equipe Traitement de l'information en Biologie Santé (TIBS - LITIS), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Xerox Research Centre Europe [Meylan], Xerox Company, and ANR-12-TECS-0006,SYNODOS,SYstème de Normalisation et d'Organisation de Données médicales textuelles pour l'Observation en Santé(2012)
Subjects: Healthcare associated infections, Adult, medicine.medical_specialty, animal structures, 020205 medical informatics, Epidemiology, computerized, Specialty, Nice, Health Informatics, 02 engineering and technology, Decision support systems, Healthcare-associated infections, computer.software_genre, Sensitivity and Specificity, Hospitals, University, 03 medical and health sciences, Clinical, 0302 clinical medicine, Intensive care, 0202 electrical engineering, electronic engineering, information engineering, medicine, Electronic Health Records, Humans, [INFO]Computer Science [cs], 030212 general & internal medicine, computer.programming_language, Cross Infection, business.industry, Digestive surgery, Medical record, Natural language processing, Medical records systems, virus diseases, University hospital, 3. Good health, Intensive Care Units, Artificial intelligence, business, computer, Algorithms
Abstract: Objective There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents. Materials and methods The collection of textual records in these hospitals was carried out between October 2009 and December 2010 in three French University hospitals (Lyon, Rouen and Nice). The following medical specialties were included in the study: digestive surgery, neurosurgery, orthopedic surgery, adult intensive-care units. Reference Standard surveillance was compared with the results of automatic detection using NLP. Sensitivity on 56 HAI cases and specificity on 57 non-HAI cases were calculated. Results The accuracy rate was 84% (n = 95/113). The overall sensitivity of automatic detection of HAIs was 83.9% (CI 95%: 71.7–92.4) and the specificity was 84.2% (CI 95%: 72.1–92.5). The sensitivity varies from one specialty to the other, from 69.2% (CI 95%: 38.6–90.9) for intensive care to 93.3% (CI 95%: 68.1–99.8) for orthopedic surgery. The manual review of classification errors showed that the most frequent cause was an inaccurate temporal labeling of medical events, which is an important factor for HAI detection. Conclusion This study confirmed the feasibility of using NLP for the HAI detection in hospital facilities. Automatic HAI detection algorithms could offer better surveillance standardization for hospital comparisons.
Published: 2018
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5. Sequence-based Structured Prediction for Semantic Parsing

Author: Claire Gardent, Chunyang Xiao, Marc Dymetman, Xerox Research Centre Europe [Meylan], Xerox Company, Natural Language Processing : representations, inference and semantics (SYNALP), Department of Natural Language Processing & Knowledge Discovery (LORIA - NLPKD), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Sequence, Parsing, Artificial neural network, Computer science, Computer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing), 02 engineering and technology, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Prefix, TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES, ACM: H.: Information Systems, Symbol (programming), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Structured prediction, Representation (mathematics), Algorithm, computer, Encoder, 0105 earth and related environmental sciences
Abstract: International audience; We propose an approach for semantic parsing that uses a recurrent neural network to map a natural language question into a logical form representation of a KB query. Building on recent work by (Wang et al., 2015), the interpretable logical forms, which are structured objects obeying certain constraints, are enumerated by an underlying grammar and are paired with their canonical realizations. In order to use sequence prediction, we need to sequentialize these logical forms. We compare three sequentializations: a direct linearization of the logical form, a linearization of the associated canonical realization, and a sequence consisting of derivation steps relative to the underlying grammar. We also show how grammatical constraints on the derivation sequence can easily be integrated inside the RNN-based sequential predictor. Our experiments show important improvements over previous results for the same dataset, and also demonstrate the advantage of incorporating the grammatical constraints.
Published: 2016
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6. Local Convolutional Features with Unsupervised Training for Image Retrieval

Author: Julien Mairal, Cordelia Schmid, Matthijs Douze, Mattis Paulin, Florent Perronin, Zaid Harchaoui, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, ERC_Allegro, ANR_Macaron, CNRS_Mastodons_Titan, Xerox Research Center Europe, ANR-14-CE23-0003,MACARON,Apprentissage statistique à grande échelle et applications(2014), and European Project: 320559,EC:FP7:ERC,ERC-2012-ADG_20120216,ALLEGRO(2013)
Subjects: Computer science, business.industry, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-invariant feature transform, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, 02 engineering and technology, Machine learning, computer.software_genre, Automatic image annotation, Kernel (image processing), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Visual Word, Artificial intelligence, business, computer, Image retrieval
Abstract: International audience; Patch-level descriptors underlie several important computer vision tasks, such as stereo-matching or content-based image retrieval. We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval. The proposed family of descriptors, called Patch-CKN, adapt the recently introduced Convolutional Kernel Network (CKN), an unsupervised framework to learn convolutional architectures. We present a comparison framework to benchmark current deep convolutional approaches along with Patch-CKN for both patch and image retrieval, including our novel ``RomePatches'' dataset. Patch-CKN descriptors yield competitive results compared to supervised CNNs alternatives on patch and image retrieval.
Published: 2015
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7. Explicit embeddings for nearest neighbor search with Mercer kernels

Author: Hervé Jégou, Rémi Gribonval, Patrick Pérez, Florent Perronnin, Anthony Bourrier, GIPSA - Vision and Brain Signal Processing (GIPSA-VIBS), Département Images et Signal (GIPSA-DIS), Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Stendhal - Grenoble 3-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio (PANAMA), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Technicolor [Cesson Sévigné], Technicolor, Xerox Research Centre Europe [Meylan], Xerox Company, Creating and exploiting explicit links between multimedia fragments (LinkMedia), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-MEDIA ET INTERACTIONS (IRISA-D6), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Stendhal - Grenoble 3-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)
Subjects: Statistics and Probability, Explicit embeddings, Nearest neighbor search, Machine learning, computer.software_genre, Margin (machine learning), Encoding (memory), Mathematics, Euclidean space, business.industry, Applied Mathematics, Nearest Neighbor search, Condensed Matter Physics, Euclidean distance, Modeling and Simulation, Kernel (statistics), [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Embedding, Mercer kernels, Geometry and Topology, Computer Vision and Pattern Recognition, Artificial intelligence, Fixed-radius near neighbors, business, computer, Algorithm
Abstract: International audience; Many approximate nearest neighbor search algorithms operate under memory constraints, by computing short signatures for database vectors while roughly keeping the neighborhoods for the distance of interest. Encoding procedures designed for the Euclidean distance have attracted much attention in the last decade.In the case where the distance of interest is based on a Mercer kernel, we propose a simple, yet effective two-step encoding scheme: first, compute an explicit embedding to map the initial space into a Euclidean space; second, apply an encoding step designed to work with the Euclidean distance. Comparing this simple baseline with existing methods relying on implicit encoding, we demonstrate better search recall for similar code sizes with the chi-square kernel in databases comprised of visual descriptors, outperforming concurrent state-of-the-art techniques by a large margin.
Published: 2015
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8. Revisiting the Fisher vector for fine-grained classification

Author: Naila Murray, Philippe-Henri Gosselin, Hervé Jégou, Florent Perronnin, Multimedia content-based indexing (TEXMEX), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Multimedia Indexation and Data Integration (MIDI), Equipes Traitement de l'Information et Systèmes (ETIS - UMR 8051), Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY)-Ecole Nationale Supérieure de l'Electronique et de ses Applications (ENSEA)-Centre National de la Recherche Scientifique (CNRS)-CY Cergy Paris Université (CY), Xerox Research Centre Europe [Meylan], Xerox Company, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, and Gosselin, Philippe-Henri
Subjects: Contextual image classification, Computer science, business.industry, Fisher kernel, Search engine indexing, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, Fisher vector, 02 engineering and technology, Machine learning, computer.software_genre, [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Artificial Intelligence, Signal Processing, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, business, computer, Software
Abstract: Wining method of Fine-grain image classification challenge 2013.Late combination of two indexing and classification strategies.Good practices for fine grain image classification.Key features: descriptors filtering, spatial coordinates coding, active learning. This paper describes the joint submission of Inria and Xerox to their joint participation to the FGCOMP'2013 challenge. Although the proposed system follows most of the standard Fisher classification pipeline, we describe a few key features and good practices that significantly improve the accuracy when specifically considering fine-grain classification tasks. In particular, we consider the late fusion of two systems both based on Fisher vectors, but for which we choose drastically design choices that make them very complementary. Moreover, we propose a simple yet effective filtering strategy, which significantly boosts the performance for several class domains.
Published: 2014

9. Transformation Pursuit for Image Classification

Author: Zaid Harchaoui, Cordelia Schmid, Jerome Revaud, Florent Perronnin, Mattis Paulin, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, ERC_Allegro, AXES, ANR-11-LABX-0025,PERSYVAL-lab,Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique(2011), ANR-12-CORD-0016,FIRE-ID,Reconnaissance à grain fin dans les grandes bases de données d'images(2012), European Project: 320559,EC:FP7:ERC,ERC-2012-ADG_20120216,ALLEGRO(2013), and European Project: 269980,EC:FP7:ICT,FP7-ICT-2009-6,AXES(2011)
Subjects: Training set, Contextual image classification, Selection (relational algebra), business.industry, Training time, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Machine learning, computer.software_genre, Image (mathematics), Transformation (function), Simple (abstract algebra), Artificial intelligence, Noise (video), business, computer, Mathematics
Abstract: International audience; A simple approach to learning invariances in image classification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a compact subset is challenging. Indeed, all transformations are not equally informative and adding uninformative transformations increases training time with no gain in accuracy. We propose a principled algorithm – Image Transformation Pursuit (ITP) – for the automatic selection of a compact set of transformations. ITP works in a greedy fashion, by selecting at each iteration the one that yields the highest accuracy gain. ITP also allows to efficiently explore complex transformations, that combine basic transformations. We report results on two public benchmarks: the CUB dataset of bird images and the ImageNet 2010 challenge. Using Fisher Vector representations, we achieve an improvement from 28.2% to 45.2% in top-1 accuracy on CUB, and an improvement from 70.1% to 74.9% in top-5 accuracy on ImageNet. We also show significant improvements for deep convnet features: from 47.3% to 55.4% on CUB and from 77.9% to 81.4% on ImageNet.
Published: 2014
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10. Instance classification with prototype selection

Author: Hervé Jégou, Florent Perronnin, Josip Krapac, Teddy Furon, Multimedia content-based indexing (TEXMEX), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria), Xerox Research Centre Europe [Meylan], Xerox Company, Xerox Research Center Europe, ANR-12-CORD-0016,FIRE-ID,Reconnaissance à grain fin dans les grandes bases de données d'images(2012), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Inria Rennes – Bretagne Atlantique
Subjects: Contextual image classification, business.industry, Computer science, media_common.quotation_subject, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Feature selection, Fisher vector, Classification scheme, Pattern recognition, Machine learning, computer.software_genre, Logos Bible Software, Hamming embedding, ComputingMethodologies_PATTERNRECOGNITION, Voting, Artificial intelligence, business, computer, Selection (genetic algorithm), media_common
Abstract: International audience; We address the problem of instance classification: our goal is to annotate images with tags corresponding to objects classes which exhibit small intra-class variations such as logos, products or landmarks. We propose a novel algorithm for the selection of class-specific prototypes which are used in a voting-based classification scheme. We show significant improvements over two state-of-the-art methods, namely the Fisher vector and Hamming Embedding, on two challenging methods of logos and vehicles.
Published: 2014
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11. Translation project adaptation for MT-enhanced computer assisted translation

Author: Nicola Bertoldi, Mauro Cettolo, Marcello Federico, Christophe Servan, Loïc Barrault, Holger Schwenk, Fondazione Bruno Kessler [Trento, Italy] (FBK), Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM), Xerox Research Centre Europe [Meylan], Xerox Company, and European Project: 287688,EC:FP7:ICT,FP7-ICT-2011-7,MATECAT(2011)
Subjects: Linguistics and Language, Computer science, 02 engineering and technology, computer.software_genre, Language and Linguistics, German, 030507 speech-language pathology & audiology, 03 medical and health sciences, Translation project, Artificial Intelligence, Robustness (computer science), 0202 electrical engineering, electronic engineering, information engineering, Language industry, business.industry, Information technology, Transfer-based machine translation, language.human_language, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, language, Computer-assisted translation, 020201 artificial intelligence & image processing, Artificial intelligence, Computational linguistics, 0305 other medical science, business, computer, Software, Natural language processing
Abstract: The effective integration of MT technology into computer-assisted translation tools is a challenging topic both for academic research and the translation industry. In particular, professional translators consider the ability of MT systems to adapt to the feedback provided by them to be crucial. In this paper, we propose an adaptation scheme to tune a statistical MT system to a translation project using small amounts of post-edited texts, like those generated by a single user in even just one day of work. The same scheme can be applied on a larger scale in order to focus general purpose models towards the specific domain of interest. We assess our method on two domains, namely information technology and legal, and four translation directions, from English to French, Italian, Spanish and German. The main outcome is that our adaptation strategy can be very effective provided that the seed data used for adaptation is `close enough' to the remaining text to be translated; otherwise, MT quality neither improves nor worsens, thus showing the robustness of our method.
Published: 2014
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12. Distance-Based Image Classification: Generalizing to new classes at near-zero cost

Author: Jakob Verbeek, Thomas Mensink, Gabriela Csurka, Florent Perronnin, Intelligent Systems Lab. (ISLA), University of Amsterdam [Amsterdam] (UvA), Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, Amsterdam Machine Learning lab (IVI, FNWI), and Intelligent Sensory Information Systems (IVI, FNWI)
Subjects: Computer science, Generalization, 02 engineering and technology, Machine learning, computer.software_genre, 030218 nuclear medicine & medical imaging, k-nearest neighbors algorithm, Pattern Recognition, Automated, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Computer Simulation, Image retrieval, Training set, Contextual image classification, business.industry, Applied Mathematics, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Models, Theoretical, Image Enhancement, Support vector machine, Computational Theory and Mathematics, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Artificial intelligence, Transfer of learning, business, Classifier (UML), computer, Software, Algorithms, Distance based
Abstract: International audience; We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an extension of the NCM classifier to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over 106 training images of 1,000 classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster. Furthermore, we show how a zero-shot class prior based on the ImageNet hierarchy can improve performance when few training images are available.
Published: 2013
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13. Privacy in Content-Oriented Networking: Threats and Countermeasures

Author: Abdelberi Chaabane, Ersin Uzun, Emiliano De Cristofaro, Mohamed Ali Kaafar, Privacy Models, Architectures and Tools for the Information Society (PRIVATICS), CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria), Palo Alto Research Center (PARC), Xerox Company, Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Inria Lyon
Subjects: Networking and Internet Architecture (cs.NI), FOS: Computer and information sciences, Network architecture, Information privacy, Computer Science - Cryptography and Security, Privacy by Design, Computer Networks and Communications, Computer science, business.industry, Privacy software, Internet privacy, Computer security, computer.software_genre, Networking hardware, Computer Science - Networking and Internet Architecture, [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR], Scalability, The Internet, business, Cryptography and Security (cs.CR), computer, Software, Anonymity
Abstract: International audience; As the Internet struggles to cope with scalability, mobility, and security issues, new network architectures are being proposed to better accommodate the needs of modern systems and applications. In particular, Content-Oriented Networking (CON) has emerged as a promising next-generation Internet architecture: it sets to decouple content from hosts, at the network layer, by naming data rather than hosts. CON comes with a potential for a wide range of benefits, including reduced congestion and improved delivery speed by means of content caching, simpler configuration of network devices, and security at the data level. However, it remains an interesting open question whether or not, and to what extent, this emerging networking paradigm bears new privacy challenges. In this paper, we provide a systematic privacy analysis of CON and the common building blocks among its various architectural instances in order to highlight emerging privacy threats, and analyze a few potential countermeasures. Finally, we present a comparison between CON and today's Internet in the context of a few privacy concepts, such as, anonymity, censoring, traceability, and confidentiality
Published: 2013

14. Label-Embedding for Attribute-Based Classification

Author: Zaid Harchaoui, Cordelia Schmid, Zeynep Akata, Florent Perronnin, Xerox Research Centre Europe [Meylan], Xerox Company, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: Training set, Contextual image classification, business.industry, Computer science, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, 02 engineering and technology, Machine learning, computer.software_genre, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Embedding, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract: International audience; Attributes are an intermediate representation, which enables parameter sharing between classes, a must when training data is scarce. We propose to view attribute-based image classification as a label-embedding problem: each class is embedded in the space of attribute vectors. We introduce a function which measures the compatibility between an image and a label embedding. The parameters of this function are learned on a training set of labeled samples to ensure that, given an image, the correct classes rank higher than the incorrect ones. Results on the Animals With Attributes and Caltech-UCSD-Birds datasets show that the proposed framework outperforms the standard Direct Attribute Prediction baseline in a zero-shot learning scenario. The label embedding framework offers other advantages such as the ability to leverage alternative sources of information in addition to attributes (e.g. class hierarchies) or to transition smoothly from zero-shot learning to learning with large quantities of data.
Published: 2013
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15. Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

Author: Mensink, T., Verbeek, J., Perronnin, F., Csurka, G., Farinella, G.M., Battiato, S., Cipolla, R, Xerox Research Centre Europe [Meylan], Xerox Company, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Farinella, Giovanni Maria and Battiato, Sebastiano and Cipolla, Roberto, Amsterdam Machine Learning lab (IVI, FNWI), and Intelligent Sensory Information Systems (IVI, FNWI)
Subjects: Contextual image classification, business.industry, 05 social sciences, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, 02 engineering and technology, Machine learning, computer.software_genre, Metric (mathematics), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, 0509 other social sciences, 050904 information & library sciences, business, Image retrieval, Classifier (UML), computer, Mathematics, Distance based
Abstract: International audience; Many real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. Since the performance of distance-based classifiers heavily depends on the used distance function, we cast the problem into one of learning a low-rank metric, which is shared across all classes. For the NCM classifier we introduce a new metric learning approach, and we also introduce an extension to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over one million training images of thousand classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster.
Published: 2013
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16. Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost

Author: Jakob Verbeek, Gabriela Csurka, Thomas Mensink, Florent Perronnin, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, and Andrew Fitzgibbon and Svetlana Lazebnik and Pietro Perona and Yoichi Sato and Cordelia Schmid
Subjects: Training set, Contextual image classification, Generalization, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Pattern recognition, 02 engineering and technology, Machine learning, computer.software_genre, Support vector machine, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Transfer of learning, business, Reference class, Classifier (UML), computer, Mathematics
Abstract: International audience; We are interested in large-scale image classification and especially in the setting where images corresponding to new or existing classes are continuously added to the training set. Our goal is to devise classifiers which can incorporate such images and classes on-the-fly at (near) zero cost. We cast this problem into one of learning a metric which is shared across all classes and explore k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. We learn metrics on the ImageNet 2010 challenge data set, which contains more than 1.2M training images of 1K classes. Surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier, and has comparable performance to linear SVMs. We also study the generalization performance, among others by using the learned metric on the ImageNet-10K dataset, and we obtain competitive performance. Finally, we explore zero-shot classification, and show how the zero-shot model can be combined very effectively with small training datasets.
Published: 2012
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17. Towards good practice in large-scale learning for image classification

Author: Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid, Xerox Research Centre Europe [Meylan], Xerox Company, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), and Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)
Subjects: Early stopping, Contextual image classification, business.industry, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, 02 engineering and technology, 010501 environmental sciences, Machine learning, computer.software_genre, 01 natural sciences, Regularization (mathematics), Ranking (information retrieval), Support vector machine, Stochastic gradient descent, 0202 electrical engineering, electronic engineering, information engineering, Benchmark (computing), 020201 artificial intelligence & image processing, Stochastic optimization, Artificial intelligence, business, computer, 0105 earth and related environmental sciences, Mathematics
Abstract: International audience; We propose a benchmark of several objective functions for large-scale image classification: we compare the one- vs-rest, multiclass, ranking and weighted average ranking SVMs. Using stochastic gradient descent optimization, we can scale the learning to millions of images and thousands of classes. Our experimental evaluation shows that ranking based algorithms do not outperform a one-vs-rest strategy and that the gap between the different algorithms reduces in case of high-dimensional data. We also show that for one-vs-rest, learning through cross-validation the optimal degree of imbalance between the positive and the negative samples can have a significant impact. Furthermore, early stopping can be used as an effective regularization strategy when training with stochastic gradient algorithms. Follow- ing these "good practices", we were able to improve the state-of-the-art on a large subset of 10K classes and 9M of images of ImageNet from 16.7% accuracy to 19.1%.
Published: 2012
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18. Tree-structured CRF models for interactive image labeling

Author: Jakob Verbeek, Thomas Mensink, Gabriela Csurka, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: Computer science, 02 engineering and technology, Documentation, computer.software_genre, Machine learning, Sensitivity and Specificity, Pattern Recognition, Automated, User-Computer Interface, Artificial Intelligence, Image Interpretation, Computer-Assisted, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), Structured prediction, Contextual image classification, Standard test image, business.industry, Applied Mathematics, Cognitive neuroscience of visual object recognition, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Reproducibility of Results, 020206 networking & telecommunications, Image Enhancement, Image labeling, Radiology Information Systems, Computational Theory and Mathematics, 020201 artificial intelligence & image processing, Computer Vision and Pattern Recognition, Data mining, Artificial intelligence, business, computer, Software, Algorithms
Abstract: International audience; We propose structured prediction models for image labeling that explicitly take into account dependencies among image labels. In our tree structured models, image labels are nodes, and edges encode dependency relations. To allow for more complex dependencies, we combine labels in a single node, and use mixtures of trees. Our models are more expressive than independent predictors, and lead to more accurate label predictions. The gain becomes more significant in an interactive scenario where a user provides the value of some of the image labels at test time. Such an interactive scenario offers an interesting trade-off between label accuracy and manual labeling effort. The structured models are used to decide which labels should be set by the user, and transfer the user input to more accurate predictions on other image labels. We also apply our models to attribute-based image classification, where attribute predictions of a test image are mapped to class probabilities by means of a given attribute-class mapping. Experimental results on three publicly available benchmark data sets show that in all scenarios our structured models lead to more accurate predictions, and leverage user input much more effectively than state-of-the-art independent models.
Published: 2012
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19. Learning Multiple Tasks with Boosted Decision Trees

Author: Boris Chidlovskii, Rémi Gilleron, Fabien Torre, Jean Baptiste Faddoul, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, Modeling Tree Structures, Machine Learning, and Information Extraction (MOSTRARE), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), and Lecture Note in Computer Science
Subjects: Incremental decision tree, Information Gain, Computer science, business.industry, Decision tree learning, Decision Trees, ID3 algorithm, Decision tree, Multi-task learning, Machine learning, computer.software_genre, Boosting, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Information Fuzzy Networks, Alternating decision tree, Artificial intelligence, Decision stump, business, Multi-Task Learning, computer
Abstract: International audience; We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultane- ously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost en- vironment with Multi-Task Decision Trees as weak classifiers. We first adapt the well known decision tree learning to the multi-task setting. We revise the information gain rule for learning decision trees in the multi- task setting. We use this feature to develop a novel criterion for learning Multi-Task Decision Trees. The criterion guides the tree construction by learning the decision rules from data of different tasks, and representing different degrees of task relatedness. We then modify MT-Adaboost to combine Multi-task Decision Trees as weak learners. We experimentally validate the advantage of the new technique; we report results of ex- periments conducted on several multi-task datasets, including the Enron email set and Spam Filtering collection.
Published: 2012
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20. Co-located Collaborative Visual Analytics Around a Tabletop Display

Author: Danyel Fisher, Meredith Ringel Morris, Sharoda A. Paul, Mary Czerwinski, Kori Inkpen, Petra Isenberg, Analysis and Visualization (AVIZ), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Microsoft Research [Redmond], Microsoft Corporation [Redmond, Wash.], Palo Alto Research Center (PARC), and Xerox Company
Subjects: Visual analytics, Computer science, Context (language use), 02 engineering and technology, computer.software_genre, Task (project management), Data visualization, Human–computer interaction, 0202 electrical engineering, electronic engineering, information engineering, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Cultural analytics, 050107 human factors, Collaborative software, Multimedia, business.industry, 05 social sciences, 020207 software engineering, Computer Graphics and Computer-Aided Design, Analytics, Signal Processing, Table (database), Computer Vision and Pattern Recognition, business, computer, Software
Abstract: Appeared in pre-print 12/2011; International audience; Co-located collaboration can be extremely valuable during complex visual analytics tasks. We present an exploratory study of a system designed to support collaborative visual analysis tasks on a digital tabletop display. Fifteen participant pairs employed Cambiera, a visual analytics system, to solve a problem involving 240 digital documents. Our analysis, supported by observations, system logs, questionnaires, and interview data, explores how pairs approached the problem around the table. We contribute a unique, rich understanding of how users worked together around the table and identify eight types of collaboration styles that can be used to identify how closely people work together while problem solving. We show how the closeness of teams' collaboration and communication influenced how they performed on the task overall. We further discuss the role of the tabletop for visual analytics tasks and derive design implications for future co-located collaborative tabletop problem solving systems.
Published: 2012
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21. ICDAR 2011 Book Structure Extraction Competition

Author: Gabriella Kazai, Antoine Doucet, Jean-Luc Meunier, Equipe Hultech - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), Microsoft Research [Redmond], Microsoft Corporation [Redmond, Wash.], Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: Structure (mathematical logic), Information retrieval, computer.internet_protocol, Computer science, business.industry, 05 social sciences, styling, 02 engineering and technology, Optical character recognition, computer.software_genre, Task (project management), [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Competition (economics), style, Disk formatting, component, 0202 electrical engineering, electronic engineering, information engineering, formatting, 020201 artificial intelligence & image processing, 0509 other social sciences, 050904 information & library sciences, business, computer, XML, Content management
Abstract: International audience; In this paper, we summarize the 2nd Book Structure Extraction competition run at ICDAR 2011. Its goal is to evaluate and compare automatic techniques for deriving structure information from digitized books, which could then be used to aid navigation inside the books. More specifically, the task that participants are faced with is to construct hyperlinked tables of contents for a collection of 1,000 digitized books. This paper reviews the setup of the competition, the book collection used in the task, and the measures used for the evaluation. It further presents the outcome of the competition: an additional ground truth of 513 book tables of contents, contributed by 6 institutions, and the result performance of the 4 participating research teams.
Published: 2011
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22. Learning structured prediction models for interactive image labeling

Author: Thomas Mensink, Jakob Verbeek, Gabriela Csurka, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: Contextual image classification, Standard test image, business.industry, Computer science, 02 engineering and technology, computer.software_genre, Machine learning, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Image labeling, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, Leverage (statistics), 020201 artificial intelligence & image processing, Data mining, Artificial intelligence, Structured prediction, business, computer
Abstract: International audience; We propose structured models for image labeling that take into account the dependencies among the image labels explicitly. These models are more expressive than independent label predictors, and lead to more accurate predictions. While the improvement is modest for fully-automatic image labeling, the gain is significant in an interactive scenario where a user provides the value of some of the image labels. Such an interactive scenario offers an interesting trade-off between accuracy and manual labeling effort. The structured models are used to decide which labels should be set by the user, and transfer the user input to more accurate predictions on other image labels. We also apply our models to attribute-based image classification, where attribute predictions of a test image are mapped to class probabilities by means of a given attribute-class mapping. In this case the structured models are built at the attribute level. We also consider an interactive system where the system asks a user to set some of the attribute values in order to maximally improve class prediction performance. Experimental results on three publicly available benchmark data sets show that in all scenarios our structured models lead to more accurate predictions, and leverage user input much more effectively than state-of-the-art independent models.
Published: 2011
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23. Specifying and computing preferred plans

Author: Sheila A. McIlraith, Christian Fritz, Meghyn Bienvenu, Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Distributed and heterogeneous data and knowledge (LEO), Université Paris-Sud - Paris 11 (UP11)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Palo Alto Research Center (PARC), Xerox Company, Department of Computer Science [University of Toronto] (DCS), and University of Toronto
Subjects: Linguistics and Language, Theoretical computer science, Knowledge representation and reasoning, Computer science, Semantics (computer science), 0102 computer and information sciences, 02 engineering and technology, Plan (drawing), 01 natural sciences, Language and Linguistics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Artificial Intelligence, Preferences, 0202 electrical engineering, electronic engineering, information engineering, Automated reasoning, ComputingMilieux_MISCELLANEOUS, computer.programming_language, Planning with preferences, business.industry, Planner, Preference, 010201 computation theory & mathematics, Knowledge representation, Bounded function, 020201 artificial intelligence & image processing, Artificial intelligence, Situation calculus, business, computer
Abstract: In this paper, we address the problem of specifying and computing preferred plans using rich, qualitative, user preferences. We propose a logical language for specifying preferences over the evolution of states and actions associated with a plan. We provide a semantics for our first-order preference language in the situation calculus, and prove that progression of our preference formulae preserves this semantics. This leads to the development of PPlan, a bounded best-first search planner that computes preferred plans. Our preference language is amenable to integration with many existing planners, and beyond planning, can be used to support a diversity of dynamical reasoning tasks that employ preferences.
Published: 2011
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24. On data fusion in information retrieval using different aggregation operators

Author: Julien Ah-Pine, Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: Multicriteria decision, fuzzy results merging, Computer Networks and Communications, Computer science, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Context (language use), metasearch, 02 engineering and technology, computer.software_genre, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Search engine, Operator (computer programming), Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, unsupervised rank aggregation, data fusion, Information retrieval, Rank (computer programming), Sensor fusion, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], aggregation operator, 020201 artificial intelligence & image processing, Data mining, Metasearch engine, computer, Software
Abstract: International audience; This paper is concerned with the problem of unsupervised rank aggregation in the context of metasearch in information retrieval. In such tasks, we are given many partial ordered lists of retrieved items provided by many search engines and we want to define a way for aggregating those lists in order to find out a consensus. One classical approach consists in aggregating, for each retrieved item, the scores given by the different search engines. Then, we use the resulting aggregated scores distribution in order to infer a consensus ordered list. In this paper we investigate whether aggregation operators defined in the fields of multi-sensor fusion and multicriteria decision making are of interest for metasearch problems or not. Moreover, another purpose of this paper is to introduce a new aggregation operator, its foundations and its properties. We finally test all these aggregation operators for metasearch tasks using the Letor 2.0 dataset. Our results show that among the studied aggregation functions, the ones which are more compensatory outperform the baseline methods CombSUM and CombMNZ.
Published: 2011
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25. Boosting Multi-Task Weak Learners with Applications to Textual and Social Data

Author: Fabien Torre, Boris Chidlovskii, Rémi Gilleron, Jean Baptiste Faddoul, Xerox Research Centre Europe [Meylan], Xerox Company, Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS), Modeling Tree Structures, Machine Learning, and Information Extraction (MOSTRARE), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), Groupe de Recherche en Apprentissage Automatique (GRAppA - LIFL), and Université de Lille, Sciences et Technologies-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)
Subjects: Boosting (machine learning), business.industry, Computer science, Decision trees, Decision tree, Multi-task learning, Textual and social data, Machine learning, computer.software_genre, Boosting, Support vector machine, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Decision stump, Artificial intelligence, AdaBoost, business, Classifier (UML), computer
Abstract: International audience; Learning multiple related tasks from data simultaneously can improve predictive performance relative to learning these tasks independently. In this paper we propose a novel multi-task learning algorithm called MT-Adaboost: it extends Adaboost algorithm to the multi-task setting; it uses as multi-task weak classifier a multi-task decision stump. This allows to learn different dependencies between tasks for different regions of the learning space. Thus, we relax the conventional hypothesis that tasks behave similarly in the whole learning space. Moreover, MT-Adaboost can learn multiple tasks without imposing the constraint of sharing the same label set and/or examples between tasks. A theoretical analysis is derived from the analysis of the original Adaboost. Experiments for multiple tasks over large scale textual data sets with social context (Enron and Tobacco) give rise to very promising results.
Published: 2010

26. Improving the Fisher Kernel for Large-Scale Image Classification

Author: Florent Perronnin, Thomas Mensink, Jorge Sanchez, Xerox Research Centre Europe [Meylan], Xerox Company, Learning and recognition in vision (LEAR), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS), and Kostas Daniilidis and Petros Maragos and Nikos Paragios
Subjects: Contextual image classification, business.industry, Fisher kernel, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Scale-invariant feature transform, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 020207 software engineering, Fisher vector, Pattern recognition, 02 engineering and technology, Pascal (programming language), Mixture model, Machine learning, computer.software_genre, ComputingMethodologies_PATTERNRECOGNITION, Discriminative model, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, computer.programming_language, Mathematics
Abstract: International audience; The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we compare two abundant resources of labeled images to learn classifiers: ImageNet and Flickr groups. In an evaluation involving hundreds of thousands of training images we show that classifiers learned on Flickr groups perform surprisingly well (although they were not intended for this purpose) and that they can complement classifiers learned on more carefully annotated datasets.
Published: 2010
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27. Virtual screening with support vector machines and structure kernels

Author: Pierre Mahé, Jean-Philippe Vert, Xerox Research Centre Europe [Meylan], Xerox Company, Cancer et génome: Bioinformatique, biostatistiques et épidémiologie d'un système complexe, MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Bioinformatique (CBIO), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Vert, Jean-Philippe, Mines Paris - PSL (École nationale supérieure des mines de Paris), Institut Curie [Paris]-MINES ParisTech - École nationale supérieure des mines de Paris, and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: FOS: Computer and information sciences, Graph kernel, Databases, Factual, Computer science, Drug Evaluation, Preclinical, Ligands, computer.software_genre, Quantitative Biology - Quantitative Methods, 01 natural sciences, Machine Learning (cs.LG), Polynomial kernel, Drug Discovery, [CHIM.CHEM] Chemical Sciences/Cheminformatics, MESH: Ligands, Quantitative Methods (q-bio.QM), [INFO.INFO-BI] Computer Science [cs]/Bioinformatics [q-bio.QM], 0303 health sciences, [SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], General Medicine, [SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM], Computer Science Applications, Kernel method, Pharmaceutical Preparations, Kernel (statistics), Radial basis function kernel, MESH: Drug Evaluation, Preclinical, Data mining, Tree kernel, Algorithms, [CHIM.CHEM]Chemical Sciences/Cheminformatics, MESH: Pharmaceutical Preparations, MESH: Algorithms, Machine learning, 03 medical and health sciences, MESH: Computer Simulation, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Artificial Intelligence, Least squares support vector machine, MESH: Artificial Intelligence, Computer Simulation, 030304 developmental biology, business.industry, Organic Chemistry, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], MESH: Databases, Factual, 0104 chemical sciences, Support vector machine, Computer Science - Learning, 010404 medicinal & biomolecular chemistry, FOS: Biological sciences, Artificial intelligence, [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM], business, computer
Abstract: International audience; Support vector machines and kernel methods have recently gained considerable attention in chemoinformatics. They offer generally good performance for problems of supervised classification or regression, and provide a flexible and computationally efficient framework to include relevant information and prior knowledge about the data and problems to be handled. In particular, with kernel methods molecules do not need to be represented and stored explicitly as vectors or fingerprints, but only to be compared to each other through a comparison function technically called a kernel. While classical kernels can be used to compare vector or fingerprint representations of molecules, completely new kernels were developed in the recent years to directly compare the 2D or 3D structures of molecules, without the need for an explicit vectorization step through the extraction of molecular descriptors. While still in their infancy, these approaches have already demonstrated their relevance on several toxicity prediction and structure-activity relationship problems.
Published: 2009

28. Towards a methodology for named entities annotation

Author: Karën Fort, Adeline Nazarenko, Maud Ehrmann, Fort, Karën, Laboratoire d'Informatique de Paris-Nord (LIPN), Université Sorbonne Paris Cité (USPC)-Institut Galilée-Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, and Quaero
Subjects: Scheme (programming language), Information retrieval, Computer science, business.industry, [INFO.INFO-TT] Computer Science [cs]/Document and Text Processing, computer.software_genre, Task (project management), [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, Entity linking, Annotation, Named-entity recognition, Ask price, Artificial intelligence, business, computer, Natural language processing, computer.programming_language
Abstract: Poster; International audience; Today, the named entity recognition task is considered as fundamental, but it involves some specific difficulties in terms of annotation. Those issues led us to ask the fundamental question of what the annotators should annotate and, even more important, for which purpose. We thus identify the applications using named entity recognition and, according to the real needs of those applications, we propose to semantically define the elements to annotate. Finally, we put forward a number of methodological recommendations to ensure a coherent and reliable annotation scheme.
Published: 2009
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29. XTM: A Robust Temporal Text Processor

Author: Caroline Hagège, Xavier Tannier, Xerox Research Centre Europe [Meylan], Xerox Company, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), and Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)
Subjects: Computer science, 02 engineering and technology, computer.software_genre, 0202 electrical engineering, electronic engineering, information engineering, Semantic memory, Dimension (data warehouse), relations temporelles, Temporal information, traitement automatique de la langue, 060201 languages & linguistics, Information retrieval, business.industry, Information processor, 06 humanities and the arts, Noun phrase, extraction d'information, [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, TAL, Research centre, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0602 languages and literature, temps, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer, Natural language processing
Abstract: International audience; We present in this paper the work that has been developed at Xerox Research Centre Europe to build a robust temporal text processor. The aim of this processor is to extract events described in texts and to link them, when possible, to a temporal anchor. Another goal is to be able to establish temporal ordering between the events expressed in texts. One of the originalities of this work is that the temporal processor is coupled with a syntactic-semantic analyzer. The temporal module takes then advantage of syntactic and semantic information extracted from text and at the same time, syntactic and semantic processing benefits from the temporal processing performed. As a result, analysis and management of temporal information is combined with other kinds of syntactic and semantic information, making possible a more refined text understanding processor that takes into account the temporal dimension.
Published: 2008

30. XRCE-T: XIP Temporal Module for TempEval Campaign

Author: Xavier Tannier, Caroline Hagège, Xerox Research Centre Europe [Meylan], Xerox Company, Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris Saclay (COmUE)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université - UFR d'Ingénierie (UFR 919), and Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11)
Subjects: Computer science, business.industry, expressions temporelles, computer.software_genre, Syntax, Traitement automatique des langues, Extraction d'information, Complement (complexity), [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing, TAL, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], temps, Computer vision, Artificial intelligence, business, computer, Natural language processing
Abstract: http://www.limsi.fr/Individu/xtannier/Publications/Tannier_TempEval07_abstract.html; International audience; We present the system we used for the TempEval competition. This system relies on a deep syntactic analyzer that has been extended for the treatment of temporal ex-pressions. So, together with the temporal treatment needed for TempEval purposes, further syntactico-semantic information is also calculated, making thus temporal processing a complement for a better gen-eral purpose text understanding system.
Published: 2007

31. Logic-Based XPath Optimization

Author: Jean-Yves Vion-Dury, Pierre Genevès, Web, adaptation and multimedia (WAM), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: ACM: H.: Information Systems/H.2: DATABASE MANAGEMENT/H.2.4: Systems/H.2.4.5: Query processing, Computer science, computer.internet_protocol, Schematron, 02 engineering and technology, XSLT, computer.software_genre, ACM: D.: Software/D.3: PROGRAMMING LANGUAGES/D.3.4: Processors/D.3.4.6: Optimization, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.3: Information Search and Retrieval/H.3.3.2: Query formulation, 020204 information systems, Streaming XML, 0202 electrical engineering, electronic engineering, information engineering, computer.programming_language, XPath, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], XPath 2.0, Programming language, InformationSystems_DATABASEMANAGEMENT, [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO], XQuery, XML database, XML Schema (W3C), ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, computer, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.3: Performance evaluation (efficiency and effectiveness)
Abstract: International audience; XPath was introduced by the W3C as a standard language for specifying node selection, matching conditions, and for computing values from an XML document. XPath is now used in many XML standards such as XSLT and the forthcoming XQuery database access language. Since efficient XML content querying is crucial for the performance of almost all XML processing architectures, a growing need for studying high performance XPath-based querying has emerged. Our approach aims at optimizing XPath performance through static analysis and syntactic transformation of XPath expressions.
Published: 2004
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32. XPath on left and right sides of rules

Author: Jean-Yves Vion-Dury, Web, adaptation and multimedia (WAM), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Xerox Research Centre Europe [Meylan], and Xerox Company
Subjects: XML tree, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], Programming language, Schematron, XPath 2.0, Computer science, computer.internet_protocol, ACM: D.: Software/D.3: PROGRAMMING LANGUAGES/D.3.1: Formal Definitions and Theory, XML validation, XSLT, computer.software_genre, XML Schema (W3C), XML Schema Editor, computer, computer.programming_language, XPath
Abstract: XPath [3, 5] is a powerful and quite successful language able to perform complex node selection in trees through compact specifications. As such, it plays a growing role in many areas ranging from schema specifications, designation and transformation languages to XML query languages. Moreover, researchers have proposed elegant and tractable formal semantics [8, 9, 10, 14], fostering various works on mathematical properties and theoretical tools [10, 13, 12, 14].We propose here a novel way to consider XPath, not only for selecting nodes, but also for tree rewriting using rules. In the rule semantics we explore, XPath expressions (noted p,p') are used both on the left and on the right side (i.e. rules have the form p → p'). We believe that this proposal opens new perspectives toward building highly concise XML transformation languages on widely accepted basis.
Published: 2003
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33. Semantically-based text authoring and the concurrent documentation of experimental protocols

Author: Marc Dymetman, Eric Fanchon, Sylvain Pogodalla, Caroline Brun, Stanislas Lhomme, Xerox Research Centre Europe [Meylan], Xerox Company, Institut de biologie structurale (IBS - UMR 5075 ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Linear logic, proof networks and categorial grammars (CALLIGRAMME), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS), Cécile Roisin and Ethan V. Munson and Christine Vanoirbeek, Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut de Recherche Interdisciplinaire de Grenoble (IRIG), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Lorraine (INPL)-Université Nancy 2-Université Henri Poincaré - Nancy 1 (UHP), and Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche Interdisciplinaire de Grenoble (IRIG)
Subjects: Computer science, computer.internet_protocol, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Semantics, World Wide Web, 03 medical and health sciences, Documentation, Application domain, Authoring system, contenu, content, sémantique, Protocol (object-oriented programming), semantics, 030304 developmental biology, 0303 health sciences, business.industry, 030302 biochemistry & molecular biology, rédaction, Natural language generation, authoring, document, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Computational linguistics, Software engineering, business, computer, XML
Abstract: Colloque avec actes et comité de lecture. internationale./http://portal.acm.org/citation.cfm?id=958220.958256&coll=portal&dl=ACM&type=series&idx=958220&part=Proceedings&WantType=Proceedings&title=Document%20Engineering&CFID=1928112&CFTOKEN=91775967; International audience; We describe an application of controlled text authoring to biological experiment reports. This work is the result of a collaboration between a computational linguistics team and biological experts from a startup company specializing in protein production studies. We start by presenting our semantically-controlled authoring system, MDA (Multilingual Document Authoring), an expressive model for specifying well-formedness conditions both at the level of the document content and at the level of its textual realization. We then discuss the practical needs of experiment documentation in bioengineering. We then go on to describe the prototype we have developed for this application domain, along with a preliminary evaluation. Finally we discuss a promising new idea emerging from the experimentation but which seems of wider applicability: how the authoring system represents a step towards integrating the formalization of an experimental protocol with its associated textual documentation.
Published: 2003
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34. Experimenting with the circus language for XML modeling and transformation

Author: Veronika Lux, Emmanuel Pietriga, Jean-Yves Vion-Dury, Xerox Research Centre Europe [Meylan], Xerox Company, Tools for Electronic Documents, Research and applications (OPERA), Inria Grenoble - Rhône-Alpes, and Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Document Structure Description, [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], Programming language, Computer science, computer.internet_protocol, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Efficient XML Interchange, 020207 software engineering, XML validation, 02 engineering and technology, Document type definition, computer.file_format, computer.software_genre, XML database, XML Schema Editor, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, XML schema, computer, XML, computer.programming_language
Abstract: After a brief introduction to the Circus programming language, we present a simple type set to model XML structures. We then describe a transformation that takes a mail as input and produces a reply, showing how subtyping is used in order to refine the type control and specialize the transformation. Conclusions are drawn both on our (easy to use but clearly limited) XML data model and on Circus itself ; expected qualities of the language are verified ; the need for some new features is expressed. Finally, we sketch some language extensions, a richer model for XML structures, and explain our choices and expectations.
Published: 2002
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35. VXT: A Visual Approach to XML Transformations

Author: Emmanuel Pietriga, Jean-Yves Vion-Dury, Vincent Quint, Tools for Electronic Documents, Research and applications (OPERA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Xerox Research Centre Europe [Meylan], Xerox Company, Institut d'Informatique et de Mathématiques Appliquées de Grenoble (IMAG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Document Structure Description, Programming language, computer.internet_protocol, Schematron, Computer science, [INFO.INFO-WB]Computer Science [cs]/Web, 020207 software engineering, XML validation, 02 engineering and technology, XSLT, computer.software_genre, XML framework, XML database, Streaming XML, 0202 electrical engineering, electronic engineering, information engineering, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], computer, XML, computer.programming_language
Abstract: The domain of XML transformations is becoming more and more important as a result of the increasing number of applications adopt- ing XML as their format for data exchange or representation. Most of the existing solutions for expressing XML transformations are textual languages, such as XSLT or DOM combined with a general- purpose programming language. Several tools build on top of these languages, providing a graphical environment. Transformations are however still specified in a textual way using the underlying lan- guage (often XSLT), thus requiring the user to learn the associated textual language. We believe that visual programming techniques are well-suited to representing XML structures and make the specification of trans- formations simpler. We present a visual programming language for the specification of XML transformations in an interactive environ- ment, based on a zoomable user interface toolkit. Transformations can be run from the application or exported to two target languages: XSLT and Circus, a general-purpose structure transformation lan- guage designed by the second author and briefly introduced in this paper.
Published: 2001
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36. VXT: Visual XML Transformer

Author: Jean-Yves Vion-Dury, Emmanuel Pietriga, Xerox Research Centre Europe [Meylan], Xerox Company, Tools for Electronic Documents, Research and applications (OPERA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), and IEEE
Subjects: Document Structure Description, XML Encryption, Computer science, Programming language, Schematron, [INFO.INFO-WB]Computer Science [cs]/Web, 020207 software engineering, XML validation, 02 engineering and technology, computer.software_genre, XML framework, Simple API for XML, XML database, Streaming XML, 0202 electrical engineering, electronic engineering, information engineering, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, 020201 artificial intelligence & image processing, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], computer
Abstract: The ever-growing amount of heterogeneous data exchanged via the Internet, combined with the popularity of XML, makes structured document transformations an increasingly important application domain. Most of the existing solutions for expressing XML transformations are textual languages, such as XSLT (Extensible Stylesheet Language Transformations) or DOM (Document Object Model), combined with a general-purpose programming language. Several tools build on top of these languages, providing a graphical environment and debugging facilities. Transformations are, however, still specified in a textual way using the underlying language (often XSLT), thus requiring users to learn it. We believe that visual programming techniques are well-suited to representing XML structures and make the specification of transformations simpler. We propose a visual language, called VXT (Visual XML Transformer), for the specification of XML transformations in an interactive environment based on a zoomable user interface toolkit and on two target languages specialised in structure transformations: Circus and XSLT.
Published: 2001
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37. A Formal Study of a Visual Language for the Visualization of Document Type Definition

Author: Jean-Yves Vion-Dury, Emmanuel Pietriga, Xerox Research Centre Europe [Meylan], Xerox Company, Tools for Electronic Documents, Research and applications (OPERA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), and IEEE
Subjects: Structure (mathematical logic), Syntax (programming languages), Programming language, business.industry, Computer science, [INFO.INFO-WB]Computer Science [cs]/Web, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], 020207 software engineering, 02 engineering and technology, Document type definition, Mathematical proof, computer.software_genre, Visualization, Visual language, 020204 information systems, Formal language, 0202 electrical engineering, electronic engineering, information engineering, Artificial intelligence, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], business, computer, Natural language processing, Visual programming language
Abstract: This formal study proposes a transformational approach to the definition of general purpose visual languages based on hierarchical structures, addressing more specifically DTD visualization as its application area. We show that such visual languages can be constructed through progressive refinement of a syntax based on nested/juxtaposed rectangles. Several transformation stages, which can all be formally characterized, produce a high quality visual representation which expresses the fundamental properties of the original structure. Moreover, this approach opens some perspectives in proving visual properties through standard mathematical tools such as inductive proofs, thus establishing some practical links between visual language theory and classical language theory.
Published: 2001
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38. A RISK ASSESSMENT SYSTEM WITH AUTOMATIC EXTRACTION OF EVENT TYPES

Author: Philippe Capet, Thomas Delavallade, Stavroula Voyatzi, Takuya Nakamura, Ágnes Sándor, Cedric Tarsitano, THALES Land & Joint Systems, THALES, Laboratoire d'Informatique Gaspard-Monge (LIGM), Centre National de la Recherche Scientifique (CNRS)-Fédération de Recherche Bézout-ESIEE Paris-École des Ponts ParisTech (ENPC)-Université Paris-Est Marne-la-Vallée (UPEM), Xerox Research Centre Europe [Meylan], Xerox Company, Mercier-Laurent, Eunikka and Leake, David, Nakamura, Takuya, Mercier-Laurent, Eunikka and Leake, David, THALES [France], and Université Paris-Est Marne-la-Vallée (UPEM)-École des Ponts ParisTech (ENPC)-ESIEE Paris-Fédération de Recherche Bézout-Centre National de la Recherche Scientifique (CNRS)
Subjects: Lexique-Grammaire, Computer science, Event (computing), business.industry, 02 engineering and technology, computer.software_genre, Machine learning, Fuzzy logic, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Information extraction, [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], 020204 information systems, Dependency grammar, 0202 electrical engineering, electronic engineering, information engineering, Early warning system, Graph (abstract data type), 020201 artificial intelligence & image processing, Artificial intelligence, Data mining, Risk assessment, business, computer, Natural language
Abstract: International audience; In this article we describe the joint effort of experts in linguistics, information extraction and risk assessment to integrate EventSpotter, an automatic event extraction engine, into ADAC, an automated early warning system. By detecting as early as possible weak signals of emerging risks ADAC provides a dynamic synthetic picture of situations involving risk. The ADAC system calculates risk on the basis of fuzzy logic rules operated on a template graph whose leaves are event types. EventSpotter is based on a general purpose natural language dependency parser, XIP, enhanced with domain-specific lexical resources (Lexicon- Grammar). Its role is to automatically feed the leaves with input data.

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