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30 results on '"MARCHESOTTI, LUCA"'

1. Wild Berry image dataset collected in Finnish forests and peatlands using drones

Author

Riz, Luigi, Povoli, Sergio, Caraffa, Andrea, Boscaini, Davide, Mekhalfi, Mohamed Lamine, Chippendale, Paul, Turtiainen, Marjut, Partanen, Birgitta, Ballester, Laura Smith, Noguera, Francisco Blanes, Franchi, Alessio, Castelli, Elisa, Piccinini, Giacomo, Marchesotti, Luca, Couceiro, Micael Santos, and Poiesi, Fabio

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands and under the canopy of Finnish forests using drones. Unlike previous and related datasets, WildBe includes new varieties of berries, such as bilberries, cloudberries, lingonberries, and crowberries, captured under severe light variations and in cluttered environments. WildBe features 3,516 images, including a total of 18,468 annotated bounding boxes. We carry out a comprehensive analysis of WildBe using six popular object detectors, assessing their effectiveness in berry detection across different forest regions and camera types. We will release WildBe publicly.

Published
2024

2. Discovering beautiful attributes for aesthetic image analysis

Author

Marchesotti, Luca, Murray, Naila, and Perronnin, Florent

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Aesthetic image analysis is the study and assessment of the aesthetic properties of images. Current computational approaches to aesthetic image analysis either provide accurate or interpretable results. To obtain both accuracy and interpretability by humans, we advocate the use of learned and nameable visual attributes as mid-level features. For this purpose, we propose to discover and learn the visual appearance of attributes automatically, using a recently introduced database, called AVA, which contains more than 250,000 images together with their aesthetic scores and textual comments given by photography enthusiasts. We provide a detailed analysis of these annotations as well as the context in which they were given. We then describe how these three key components of AVA - images, scores, and comments - can be effectively leveraged to learn visual attributes. Lastly, we show that these learned attributes can be successfully used in three applications: aesthetic quality prediction, image tagging and retrieval., Comment: IJCV, 2014

Published
2014

8. Structured context-analysis techniques in biologically inspired ambient-intelligence systems

Author

Marchesotti, Luca, Piva, Stefano, and Regazzoni, Carlo

Subjects
Intelligent devices -- Research
Abstract

In this paper, techniques and related issues for the definition of a contextual knowledge in ambient-intelligence systems are explored. A logical structure for this kind of system, inspired by a neurobiological brain model, is proposed. Through these considerations, the role and the importance of context awareness in the definition of an artificial organism showing adaptability, pervasiveness, and scalability features are described. Techniques for the definition of a multilayer context representation are explained and practically demonstrated with a test-bed. In the proposed system, a complex event classification is obtained through the fusion of heterogeneous data coming from a set of sensors thanks to the design of a self-organizing map (SOM). The SOM represents the core of the system and testing proofs show good results in the classification of the events taking place in the monitored environment. Index Terms--Context awareness, data fusion, self-organizing maps (SOMs), smart spaces.

Published
2005

9. Évaluation automatique de la qualité esthétique des photographies à l'aide de descripteurs d'images génériques

Author

Marchesotti, Luca, Perronnin, Florent, Larlus, Diane, Csurka, Gabriela, Michallon, Loïc, and Cordier, Amélie

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], descripteurs d'image, esthétique, catégorisation d'images
Abstract

Cet article s'intéresse à l'évaluation automatique des propriétés esthétiques des images. Par le passé, ce problème a été considéré à l'aide de descripteurs conçus à la main, de façon à correspondre aux bonnes pratiques photographiques (par exemple : "est-ce que cette photo est une macro ?"). Nous nous différencions fondamentalement de cette approche, et proposons d'utiliser des descripteurs d'images génériques pour l'évaluation de la qualité esthétique. Nous montrons expérimentalement que les descripteurs utilisés, qui accumulent des statistiques locales de bas niveau, encodent implicitement les propriétés utilisées explicitement par les méthodes actuellement les plus performantes, et surpassent celles-ci de façon significative

Published
2012

10. Multimodal Search for Graphic Designers

Author

Skaff, Sandra, Rouquet, David, Dellandrea, Emmanuel, Falaise, Achille, Bellynck, Valérie, Blanchon, Hervé, Boitet, Christian, Schwab, Didier, Chen, Liming, Saidi, Alexandre, Csurka, Gabriela, Marchesotti, Luca, Schwab, Didier, Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole (GETALP), 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)-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), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Université Lumière - Lyon 2 (UL2)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-CL] Computer Science [cs]/Computation and Language [cs.CL], [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience; no abstract

Published
2011

11. Classification multilingue et multimédia pour la recherche d'images dans le projet OMNIA

Author

Rouquet, David, Fallaise, Achille, Schwab, Didier, Blanchon, Hervé, Belinck, Valérie, Boitet, Christian, Dellandréa, Emmanuel, Liu, Ningning, Chen, Liming, Saidi, Alexandre, Skaff, Sandra, Marchesotti, Luca, Csurka, Gabriela, 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), 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), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Xerox Research Centre Europe [Meylan], Xerox Company, and SI LIRIS, Équipe gestionnaire des publications

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO]Computer Science [cs], [INFO] Computer Science [cs], [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

National audience; Cet article expose différents traitements pour l’indexation automatique d’images accompagnées de textes multilingues. La combinaison de ces traitements a pour but d’obtenir des descripteurs multi-facettes d’images (thématique, affectif, esthétique, etc.). Cette approche est évaluée dans le cadre du projet ANR OMNIA qui rassemble des équipes de XRCE, du LIRIS et du LIG.

Published
2010

25. AVA: A large-scale database for aesthetic visual analysis.

Author

Murray, Naila, Marchesotti, Luca, and Perronnin, Florent

Abstract

With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks. [ABSTRACT FROM PUBLISHER]

Published
2012
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