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919 results on '"Rita Cucchiara"'

901. A semi-automatic video annotation tool with MPEG-7 content collections

Author

Costantino Grana, Rita Cucchiara, Roberto Vezzani, and Daniele Bulgarelli

Subjects
video annotation, MPEG-7, content collections, Class (computer programming), Information retrieval, Computer science, Image segmentation, Metadata, Software portability, Annotation, Segmentation, Cluster analysis, Data compression
Abstract

In this work, we present a general purpose system for hierarchical structural segmentation and automatic annotation of video clips, by means of standardized low level features. We propose to automatically extract some prototypes for each class with a context based intra-class clustering. Clips are annotated following the MPEG-7 standard directives to provide easier portability. Results of automatic annotation and semi-automatic metadata creation are provided.

914. A Survey About the Cyberbullying Problem on Social Media by Using Machine Learning Approaches

Author

Giancarlo Sperlì, Carlo Sansone, Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani, Sansone, Carlo, and Sperlí, Giancarlo

Subjects
Computer science, business.industry, 02 engineering and technology, Session (web analytics), Task (project management), Identification (information), Action (philosophy), Binary classification, Human–computer interaction, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Social media, Artificial intelligence, Instant messaging, business
Abstract

The exponential growth of connected devices (i.e. laptops, smartphones or tablets) has radically changed communications means, also making it faster and impersonal by using On-line Social Networks and Instant messaging through several apps. In this paper we discuss about the cyberbullying problem, focusing on the analysis of the state-of-the-art approaches that can be classified in four different tasks (Binary Classification, Role Identification, Severity Score Computation and Incident prediction). In particular, the first task aims to predict if a particular action is aggressive or not based on the analysis of different features. In turn, the second and the third task investigate the cyberbullying problem by identifying users’ role in the exchanged message or assigning a severity score to a given users or session respectively. Nevertheless, information heterogeneity, due to different multimedia contents (i.e. text, emojis, stickers or gifs), and the use of datasets, which are typically unlabeled or manually labelled, create continuous challenges in addressing the cyberbullying problem.

Published
2021

915. Action Recognition Robust to Background Clutter by Using Stereo Vision

Author

Radu Horaud, Jan Cech, Jordi Sanchez-Riera, Interpretation and Modelling of Images and Videos (PERCEPTION), 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), Andrea Fusiello and Vittorio Murino and Rita Cucchiara, and European Project

Subjects
Sequence, Monocular, Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], 02 engineering and technology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Stereopsis, Action (philosophy), Histogram, 0202 electrical engineering, electronic engineering, information engineering, Clutter, 020201 artificial intelligence & image processing, Computer vision, Visual Word, Artificial intelligence, business, Blossom algorithm
Abstract

International audience; An action recognition algorithm which works with binocular videos is presented. The proposed method uses standard bag-of-words approach, where each action clip is represented as a histogram of visual words. However, instead of using classical monocular HoG/HoF features, we construct features from the scene-flow computed by a matching algorithm on the sequence of stereo images. The resulting algorithm has a comparable or slightly better recognition accuracy than standard monocular solution in controlled setup with a single actor present in the scene. However, we show its significantly improved performance in case of strong background clutter due to other people freely moving behind the actor.

Published
2012

916. Fusion of Speech, Faces and Text for Person Identification in TV Broadcast

Author

Viet Bac Le, Makarand Tapaswi, Guillaume Fortier, Sophie Rosset, Johann Poignant, Jakob Verbeek, Hervé Bredin, Hua Gao, Hazim Kemal Ekenel, Laurent Besacier, Claude Barras, Georges Quénot, Frédéric Jurie, Thibault Napoléon, 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), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Université Paris-Sud - Paris 11 (UP11), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Karlsruhe Institute of Technology (KIT), 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), Vocapia Research [Orsay], Vocapia, 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), Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-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 )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Modélisation et Recherche d’Information Multimédia [Grenoble] (MRIM), Equipe Image - Laboratoire GREYC - UMR6072, Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Work partly realized as part of the Quaero Program and the QCompere project, respectively funded by OSEO and ANR., Andrea Fusiello, Vittorio Murino, Rita Cucchiara, ANR-10-CORD-0103,QCOMPERE,Consortium Quaero pour la Reconnaissane Multimodale des Personnes(2010), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE), Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-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 )-Institut National Polytechnique de Grenoble (INPG)-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 )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS), and Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN)

Subjects
Computer science, Speech recognition, 020207 software engineering, 02 engineering and technology, Optical character recognition, Speaker recognition, computer.software_genre, Facial recognition system, Identification (information), ComputingMethodologies_PATTERNRECOGNITION, Broadcast television systems, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], 0202 electrical engineering, electronic engineering, information engineering, Identity (object-oriented programming), 020201 artificial intelligence & image processing, computer
Abstract

Poster session: WS21 - Workshop on Information Fusion in Computer Vision for Concept Recognition; International audience; The REPERE challenge is a project aiming at the evaluation of systems for supervised and unsupervised multimodal recognition of people in TV broadcast. In this paper, we describe, evaluate and discuss QCompere consortium submissions to the 2012 REPERE evaluation campaign dry-run. Speaker identification (and face recognition) can be greatly improved when combined with name detection through video optical character recognition. Moreover, we show that unsupervised multimodal person recognition systems can achieve performance nearly as good as supervised monomodal ones (with several hundreds of identity models).

Published
2012

917. Statistics of Pairwise Co-occurring Local Spatio-temporal Features for Human Action Recognition

Author

Piotr Bilinski, Francois Bremond, Spatio-Temporal Activity Recognition Systems (STARS), 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), This work was supported by the R egion Provence-Alpes- Côte d'Azur., and Andrea Fusiello and Vittorio Murino and Rita Cucchiara

Subjects
business.industry, Association (object-oriented programming), ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Representation (systemics), [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Pattern recognition, Machine learning, computer.software_genre, ComputingMethodologies_PATTERNRECOGNITION, Action (philosophy), Discriminative model, Statistics, Feature (machine learning), Benchmark (computing), Pairwise comparison, Artificial intelligence, business, Set (psychology), computer, Mathematics
Abstract

International audience; The bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition in videos. Together these techniques have demonstrated high recognition results for a number of action classes. Recent approaches have typically focused on capturing global statistics of features. However, existing methods ignore relations between features and thus may not be discriminative enough. Therefore, we propose a novel feature representation which captures statistics of pairwise co-occurring local spatio-temporal features. Our representation captures not only global distribution of features but also focuses on geometric and appearance (both visual and motion) relations among the features. Calculating a set of bag-of-words representations with different geometrical arrangement among the features, we keep an important association between appearance and geometric information. Using two benchmark datasets for human action recognition, we demonstrate that our representation enhances the discriminative power of features and improves action recognition performance.

Published
2012

918. A Knowledge Management System using Bayesian Network

Author

Patrizia Ribino, Salvatore Gaglio, Giuseppe Lo Re, Antonio Oliveri, Serra, R, Cucchiara, R, Roberto Serra, Rita Cucchiara, Ribino, P, Oliveri, A, Lo Re, G, and Gaglio, S

Subjects
Settore ING-INF/05 - Sistemi Di Elaborazione Delle Informazioni, Decision support system, Knowledge management, business.industry, Computer science, Data management, Knowledge engineering, Knowledge value chain, Intelligent decision support system, Document management system, Procedural knowledge, computer.software_genre, Clinical decision support system, Knowledge-based systems, Knowledge extraction, Knowledge base, Information and Communications Technology, Organizational learning, Personal knowledge management, Ontology, Domain knowledge, Knowledge Management, Bayesian Network, Artificial Intelligence, business, computer
Abstract

In today's world, decision support and knowledge management processes are strategic and interdependent activities in many organizations. The companies' interest on a correct knowledge management is grown, more than interest on the mere knowledge itself. This paper proposes a Knowledge Management System based on Bayesian networks. The system has been tested collecting and using data coming from projects and processes typical of ICT companies, and provides a Document Management System and a Decision Support system to share documents and to plan how to best use firms' knowledge.

Published
2009

919. How Do Deep Convolutional SDM Trained on Satellite Images Unravel Vegetation Ecology?

Author

Pierre Bonnet, Benjamin Deneu, Alexis Joly, François Munoz, Maximilien Servajean, Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Scientific Data Management (ZENITH), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-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), ADVanced Analytics for data SciencE (ADVANSE), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), This project has received funding from the French National Research Agency under the Investments for the Future Program, referred as ANR-16-CONV-0004 and from the European Union’s Horizon 2020 research and innovation program under grant agreement N° 863463 (Cos4Cloud project)., Alberto Del Bimbo, Rita Cucchiara, Stan Sclaroff, Giovanni Maria Farinella, Tao Mei, Marco Bertini, Hugo Jair Escalante, Roberto Vezzani, ANR-16-CONV-0004,DIGITAG,Institut Convergences en Agriculture Numérique(2016), European Project: 863463,Cos4Cloud project, Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Inria Sophia Antipolis - Méditerranée (CRISAM), and Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)

Subjects
0106 biological sciences, Computer science, Ecological Interpretation, Ecology (disciplines), Species distribution, Trait–environment relationships, 0211 other engineering and technologies, Convolutional Neural Network, 02 engineering and technology, Land cover, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 010603 evolutionary biology, 01 natural sciences, Convolutional neural network, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Environmental data, Trait-environment relationships, [SDV.EE.ECO]Life Sciences [q-bio]/Ecology, environment/Ecosystems, Altitude, Plant functional traits, [INFO]Computer Science [cs], 021101 geological & geomatics engineering, Ecology, Species distribution model (SDM), Vegetation, [SDV.BV.BOT]Life Sciences [q-bio]/Vegetal Biology/Botanics, 15. Life on land, t-SNE, Species Distribution Model, Satellite, [SDE.BE]Environmental Sciences/Biodiversity and Ecology
Abstract

International audience; Species distribution models (SDM) assess and predict how species spatial distributions depend on the environment, due to species ecological preferences. These models are used in many different scenarios such as conservation plans or monitoring of invasive species. The choice of a model and of environmental data have strong impact on the model’s ability to capture important ecological information. Specifically, state-of-the-art models generally rely on local, punctual environmental information, and do not take into account environmental variation in surrounding landscape. Here we use a convolutional neural network model to analyze and predict species distributions depending on high resolution data including remote sensing images, land cover and altitude. We show that the model unravel the functional response of vegetation to both local and large-scale environmental variation. To demonstrate the ecological significance of the results, we propose an original statistical analysis of t-SNE nonlinear dimension reduction. We illustrate and test the traits-species-environment relationships learned by the model and expressed in t-SNE dimensions.

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