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IRIM at TRECVID 2010: High Level Feature Extraction and Instance Search

David Gorisse 1 Frédéric Precioso 1 Philippe Gosselin 2 Lionel Granjon 3 Denis Pellerin 4 Michèle Rombaut 4 Hervé Bredin 5 Lionel Koenig 6 Hélène Lachambre 6 Elie El Khoury 6 Rémi Vieux 7 Boris Mansencal 7 Yifan Zhou 7 Jenny Benois-Pineau 7 Hervé Jégou 8 Stéphane Ayache 9 Bahjat Safadi 10 Yubing Tong 11 Franck Thollard 12 Georges Quénot 12 Alexandre Benoît 13 Patrick Lambert 13 
Abstract : The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2010 semantic indexing and instance search tasks. For the semantic indexing task, we evaluated a number of different descriptors and tried different fusion strategies, in particular hierarchical fusion. The best IRIM run has a Mean Inferred Average Precision of 0.0442, which is above the task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help. For the instance search task, we used only one of the example images in our queries. The rank is nearly in the middle of the list of participants. The experiment showed that HSV features outperform the concatenation of HSV and edge histograms or the wavelet feature.
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Contributor : Marie-Christine Fauvet Connect in order to contact the contributor
Submitted on : Friday, February 28, 2014 - 4:02:12 PM
Last modification on : Tuesday, October 25, 2022 - 4:16:26 PM


  • HAL Id : hal-00953839, version 1


David Gorisse, Frédéric Precioso, Philippe Gosselin, Lionel Granjon, Denis Pellerin, et al.. IRIM at TRECVID 2010: High Level Feature Extraction and Instance Search. TREC Video Retrieval Evaluation workshop, 2010, Gaithersburg, MD, United States. ⟨hal-00953839⟩



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