Motion Activity Based Semantic Video Similarity Retrieval
PCM '02 Proceedings of the Third IEEE Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Robust video sequence retrieval using a novel object-based T2D-histogram descriptor
Journal of Visual Communication and Image Representation
Robust video retrieval using temporal MVMB moments
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Robust video similarity retrieval using temporal MIMB moments
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
Effective web video clustering using playlist information
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Methods for characterizing video segments and allowing fast search in large archives are becoming essential in the video information flood. In this paper, we present a method for characterizing and clustering video segments using cumulative color histogram. The underlying assumption is that a video segment has a consistent color palette, which can be derived as a family of merged individual shot histograms. These merged histograms (SuperHistograms) are clustered using a Nearest Neighbor-clustering algorithm. Given a query video, in order to find similar videos, the SuperHistogram of the video will be generated and compared to the centers of the Nearest Neighbor clusters. The video clips in the cluster with center nearest to the query, can be searched to find video clips most similar to the query video. This method can be used in a variety of applications that need video classification and retrieval methods such as video editing, video archival, digital libraries, consumer products, and web crawling.