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Clustering video sequences in order to infer and extract activities from a single video stream is an extremely important problem and has significant potential in video indexing, surveillance, activity discovery and event recognition. Clustering a video sequence into activities requires one to simultaneously recognize activity boundaries (activity consistent subsequences) and cluster these activity subsequences. In order to do this, we build a generative model for activities (in video) using adoi:10.1109/cvpr.2007.383170 dblp:conf/cvpr/TuragaVC07 fatcat:qf6kosqku5cozexcvlx5fjgpju