VideoSense at TRECVID 2011 : Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking

Abstract : This paper describes our participation to the TRECVID 2011 challenge [1]. This year, we focused on a stacking fusion with Domain Adaptation algorithm. In machine learning, Domain Adaptation deals with learning tasks where the train and the test distributions are supposed related but different. We have implemented a classical approach for concept detection using individual features (low-level and intermediate features) and supervised classifiers. Then we combine the various classifiers with a second layer of classifier (stacking) which was specifically designed for Domain Adaptation. We show that, empirically, Domain Adaptation can improve concept detection by considering test information during the learning process.
Type de document :
Communication dans un congrès
TRECVID 2011 - TREC Video Retrieval Evaluation workshop, Nov 2011, Gaithersburg, MD, United States. NIST, 6p., 2011


https://hal.archives-ouvertes.fr/hal-00685530
Contributeur : Emilie Morvant <>
Soumis le : jeudi 5 avril 2012 - 15:13:23
Dernière modification le : mercredi 22 février 2017 - 09:28:19
Document(s) archivé(s) le : mercredi 14 décembre 2016 - 20:13:52

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  • HAL Id : hal-00685530, version 1

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Emilie Morvant, Stéphane Ayache, Amaury Habrard, Miriam Redi, Claudiu Tanase, et al.. VideoSense at TRECVID 2011 : Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking. TRECVID 2011 - TREC Video Retrieval Evaluation workshop, Nov 2011, Gaithersburg, MD, United States. NIST, 6p., 2011. <hal-00685530>

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