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Winston Hsu, Lyndon Kennedy, Shih-Fu Chang. Video Search Reranking via Information Bottleneck Principle. In ACM Multimedia, Santa Babara, CA, USA, 2006.

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We propose a novel and generic video/image reranking algorithm, IB reranking, which reorders results from text-only searches by discovering the salient visual patterns of relevant and irrelevant shots from the approximate relevance provided by text results. The IB reranking method, based on a rigorous Information Bottleneck (IB) principle, finds the optimal clustering of images that preserves the maximal mutual information between the search relevance and the high-dimensional low- level visual features of the images in the text search results. Evaluating the approach on the TRECVID 2003-2005 data sets shows significant improvement upon the text search baseline, with relative increases in average performance of up to 23%. The method requires no image search examples from the user, but is competitive with other state- of-the-art example-based approaches. The method is also highly generic and performs comparably with sophisticated models which are highly tuned for specific classes of queries, such as named-persons. Our experimental analysis has also confirmed the proposed reranking method works well when there exist sufficient recurrent visual patterns in the search results, as often the case in multi-source news videos.


Winston Hsu
Lyndon Kennedy
Shih-Fu Chang

BibTex Reference

   Author = {Hsu, Winston and Kennedy, Lyndon and Chang, Shih-Fu},
   Title = {Video Search Reranking via Information Bottleneck Principle},
   BookTitle = {ACM Multimedia},
   Address = {Santa Babara, CA, USA},
   Year = {2006}

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