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IRIM at TRECVID2009: High Level Feature Extraction

Bertrand Delezoide 1 Hervé Le Borgne 1 Pierre-Alain Moëllic 1 David Gorisse 2 Frédéric Precioso 2 Feng Wang 3 Bernard Merialdo 3 Philippe-Henri Gosselin 4 Lionel Granjon 5 Denis Pellerin 6 Michèle Rombaut 6 Hervé Bredin 7, 8 Lionel Koenig 7 Hélène Lachambre 7 Elie El Khoury 7 Boris Mansencal 9 Yifan Zhou 9 Jenny Benois-Pineau 9 Hervé Jégou 10 Stéphane Ayache 11 Bahjat Safadi 12 Georges Quénot 12 Jonathan Fabrizio 13 Matthieu Cord 2, 13 Hervé Glotin 14 Zhongqiu Zhao 14 Emilie Dumont 14 Bertrand Augereau 15 
Abstract : The IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion. The best IRIM run has a Mean Inferred Average Precision of 0.1220, which is significantly above TRECVID 2009 HLF detection 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.
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Contributor : Boris Mansencal Connect in order to contact the contributor
Submitted on : Tuesday, March 30, 2010 - 12:14:54 PM
Last modification on : Friday, August 5, 2022 - 2:45:59 PM
Long-term archiving on: : Thursday, July 1, 2010 - 8:25:41 PM


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  • HAL Id : hal-00468199, version 1


Bertrand Delezoide, Hervé Le Borgne, Pierre-Alain Moëllic, David Gorisse, Frédéric Precioso, et al.. IRIM at TRECVID2009: High Level Feature Extraction. TRECVID 2009 - TREC Video Retrieval Evaluation, Nov 2009, Gaithersburg, MD, United States. ⟨hal-00468199⟩



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