This paper describes our participation to the TRECVID 2011 challenge . 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.
VideoSense at TRECVID 2011 : Semantic indexing from light similarity functions-based domain adaptation with stacking
TRECVID 2011, 15th International Workshop on Video Retrieval Evaluation, 2011, National Institute of Standards and Technology, Gaithersburg, USA
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PERMALINK : https://www.eurecom.fr/publication/3688