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Efficiently and effectively identifying similar videos is an important and nontrivial problem in content-based video retrieval. This paper proposes a subspace symbolization approach, namely SUDS, for content-based retrieval on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the videos are processed by deriving...
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...
There is a large and rapidly increasing amount of video data on the Internet and in personal or organizational collections. Fast and accurate video search emerges to be an important issue. The need and main technical challenges for video retrieval are similar to those for the content-based image retrieval (CBIR) problem. Lack of meaningful and comprehensive text annotation means that an approach based...
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