Automatic semantic classification of video databases is very useful for users searching and browsing but it is a very challenging research problem as well. Combination of visual and text modalities is one of the key issues to bridge the semantic gap between signal and semantic. In this paper, we propose to enhance the classification of high-level concepts using intermediate topic concepts and study various fusion strategies to combine topic concepts with visual features in order to outperform unimodal classifiers. We have conducted several experiments on the TRECVID'05 collection and show here that several intermediate topic classifiers can bridge parts of the semantic gap and help to detect high-level concepts.
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