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Video Search Reranking via Information Bottleneck Principle

By Winston H. Hsu

Abstract

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

Topics: Categories and Subject Descriptors, H.3.3 [Information Search and Retrieval, Retrieval models General Terms, Algorithms, Performance, Experimentation Keywords, Video Search, Multimodal Fusion, Information Bottleneck Principle
Year: 2006
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.1854
Provided by: CiteSeerX
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