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Negative pseudo-relevance feedback in content-based video retrieval
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Negative pseudo-relevance feedback in content-based video retrieval
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Video information retrieval requires a system to find information relevant to a query which may be represented simultaneously in different ways through a text description, audio, still images and/or video sequences. We present a novel approach that uses pseudo-relevance feedback from retrieved items that are NOT similar to the query items without further inquiring user feedback. We provide insight into this approach using a statistical model and suggest a score combination scheme via posterior probability estimation. An evaluation on the 2002 TREC Video Track queries shows that this technique can improve video retrieval performance on a real collection. We believe that negative pseudo-relevance feedback shows great promise for very difficult multimedia retrieval tasks, especially when combined with other different retrieval algorithms.