Learning User Queries in Multimodal Dissimilarity Spaces
نویسندگان
چکیده
Different strategies to learn user semantic queries from dissimilarity representations of video audio-visual content are presented. When dealing with large corpora of videos documents, using a feature representation requires the online computation of distances between all documents and a query. Hence, a dissimilarity representation may be preferred because its offline computation speeds up the retrieval process. We show how distances related to visual and audio video features can directly be used to learn complex concepts from a set of positive and negative examples provided by the user. Based on the idea of dissimilarity spaces, we derive three algorithms to fuse modalities and therefore to enhance the precision of retrieval results. The evaluation of our technique is performed on artificial data and on the complete annotated TRECVID corpus.
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Interactive video retrieval based on multimodal dissimilarity representation
We present an approach to learn user semantic queries from dissimilarity representations of video audio-visual content. When dealing with large corpora of videos documents, using a feature-based representation calls for the online computation of distances between all documents and the query. Hence, a dissimilarity representation may be preferred because its offline computation speeds up the ret...
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