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Evaluation of Research Theme CogB

This document evaluates the research theme of the LEAR team at INRIA, focusing on learning and recognition in vision. Key objectives include visual recognition of objects and scenes, robust image descriptions, and statistical modeling for vision. The team’s contributions span various domains, including human motion modeling, advanced descriptors for images, and machine learning applications in object recognition. Significant progress has been made in category classification, detection, and generating impactful publications within competitive computer vision arenas.

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Evaluation of Research Theme CogB

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  1. Evaluation of Research Theme CogB

  2. Objectives • LEAR: LEArningand Recognition in vision • Visual recognition and scene understanding • Particular objects and scenes • Object classes and categories • Human motion and actions • Strategy : Robust image description + learning techniques

  3. Axes • Robust image description • Appropriate descriptors for objects and categories • Statistical modeling and machine learning for vision • Selection and adaptation of existing techniques • Visual object recognition and scene understanding • Description + learning

  4. Overview • Presentation of the team • Positioning within INRIA and internationally • Progress towards initial goals • Main scientific contributions • Future – next four years

  5. Team • Creation of the LEAR team in July 2003

  6. Positioning in INRIA • Main INRIA strategic challenge: Developing multimedia data and multimedia information processing • The only INRIA team with object recognition as its central goal • Expertise in image description and applied learning

  7. INRIA teams with related themes • Imedia: indexing, navigation and browsing in large multi-media data streams • TexMex: management of multi-media databases, handling large data collections and developing multi-media and text descriptors • Vista: analysis of image sequences, motion descriptors • Ariana: image processing for remote sensing

  8. International positioning • In France and Europe: a few groups work on the problem (Amsterdam, Oxford, Leuven, TU Darmstadt) • In the US: several groups use machine learning for visual recognition (CMU, Caltec, MIT, UBC, UCB, UCLA, UIUC) • Competitive results compared to the above groups in • Image description (scale and affine invariant regions) • Classification and localization of object categories; winner of 14 out of 18 tasks of the PASCAL object recognition challenge • Learning-based human motion modeling

  9. Progress towards initial goals • LEAR was created two and a half years ago • Significant progress towards each goal, especially • Category classification and detection • Machine learning • Scientific production • Publications (65 journals, conferences & books in 3 years, mainly in the most competitive journals and conferences) • Software, databases available on our web page • Collaborations (INRIA team MISTIS, UIUC in the US, ANU in Australia, Oxford, Leuven, LASMEA Clermont-Ferrand …)

  10. Progress towards initial goals • Industrial contracts (MBDA, Bertin technologies,Thales Optronics, Techno-Vision project Robin) • Research contracts (French grant ACI “Large quantities of data” MoviStar, EU network PASCAL, EU project AceMedia, EU project CLASS, EADS and Marie Curie postdoctoral grants) • Scientific organization (Editorial boards of PAMI and IJCV; program committees/area chairs of all major computer vision conferences; organization of ICCV’03 and CVPR’05; vice-head of AFRIF; co-ordination of EU project CLASS, Techno-Vision project Robin and ACI MoviStar)

  11. Main contributions - overview • Image descriptors • Scale- and affine-invariant detectors + descriptors • Local dense representations • Shape descriptors • Color descriptors • Learning • Clustering • Dimensionality reduction • Markov random fields • SVM kernels

  12. Main contributions - overview • Object recognition • Texture recognition • Bag-of-features representation • Spatial features (semi-local parts, hierarchical spatial model) • Multi-class hierarchical classification • Recognition with 3D models • Human detection • Human tracking and action recognition • Learning dynamical models for 2D articular human tracking • 3D human pose and motion from monocular images

  13. Invariant detectors and descriptors • Scale and affine-invariant keypoint detectors [IJCV’04] • Matching in the presence of large viewpoints changes

  14. Invariant detectors and descriptors • Evaluation of detectors and descriptors [PAMI’05, IJCV’06] • Database with different scene types (textured and structured) and transformations • Definition of evaluation criteria • Collaboration with Oxford, Leuven, Prague • Database and binaries available on the web • 4000 access and 1000 downloads

  15. Image retrieval demonstration

  16. Image retrieval demonstration

  17. Dense representation • Dense multi-scale local descriptors [ICCV’05] • Still local, but captures more of the available information • Clustering to obtain representative features • our clustering algorithm deals with very different densities • Feature selection determines the most characteristic clusters

  18. Bag-of-features for image classification SVM Extract regions Compute descriptors Find clusters and frequencies Compute distance matrix Classification

  19. bikes books building cars people phones trees Bag-of-features for image classification • Excellent results in the presence of background clutter • Our team won all image classification tasks of the PASCAL network challenge on visual object recognition

  20. A Recognition with spatial relations Approach [ICCV’05]: • Semi-local parts: point regions and similar geometric neighborhood structure • Validation, i.e. part selection • Learn a probabilistic model of the object class (discriminative maximum entropy framework)

  21. Recognition with spatial relations Improved recognition for classes with structure

  22. Human detection [CVPR’05] Histogram of oriented image gradients as image descriptor SVM as classifier, importance weighted descriptors Winner of the PASCAL challenge on human detection

  23. Human detection

  24. Evaluation of category recognition • Techno-Vision project Robin (2005-2007) • Funded by the French ministries of defence and of research • Construction of datasets and ground truth • Industrial partnership with MBDA, SAGEM, THALES, Bertin Tech, Cybernetix, EADS and CNES • Production of six datasets with thousands of annotated images, from satellite images to ground level images

  25. Evaluation of category recognition • Evaluation metrics for category classification and localization in collaboration with ONERA and CTA/DGA • Organization of competitions in 2006, 38 registered participants (research teams) at the moment • Datasets, metrics and evaluation tools will be publicly available for benchmarking

  26. Learning based human motion capture learning [CVPR’04, ICML’04, PAMI’06], best student paper at the Rank Foundation Symposium on Machine Understanding of People

  27. Learning based human motion capture

  28. Future – next four years • The major objectives remain valid • Image description [low risk] • Learn image descriptors [PhD of D. Larlus] • Shape descriptors [postdoc of V. Ferrari] • Color descriptors [postdoc of J. Van de Weijer] • Spatial relations [PhD of M. Marszalek] • Learning [medium risk] • Semi- & unsupervised learning, automatic annotation • Hierarchical structuring of categories • Existing collaborations, EU project CLASS, postdoc of J. Verbeek

  29. Future – next four years • Object recognition • Object detection & localization [low risk] • Large number of object categories [medium risk] • Scene interpretation [high risk] • Human modeling and action recognition • Pose & motion for humans in general conditions [PhD A. Agarwal] • Recognition of actions and interactions

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