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We propose a subspace symbolization approach, namely SUDS, for content-based search on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary. With this dictionary, the video data are processed using string matching techniques with two-step data simplification. A compact video representation model is developed by transforming...
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