VideoSense at TRECVID 2011 : Semantic Indexing from Light Similarity Functions-based Domain Adaptation with Stacking - Archive ouverte HAL Access content directly
Conference Papers Year : 2011

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

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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.
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Dates and versions

hal-00685530 , version 1 (05-04-2012)

Identifiers

  • HAL Id : hal-00685530 , version 1

Cite

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. 6p. ⟨hal-00685530⟩
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