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In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a bag of spatiotemporal features called video-words by quantizing the extracted 3D interest points (cuboids) from the videos. Our proposed approach is able to automatically discover the optimal number of videoword clusters by utilizing Maximization of Mutual Information(MMI). Unlike the k-means algorithm, which isdoi:10.1109/cvpr.2008.4587723 dblp:conf/cvpr/LiuS08 fatcat:4j3s2khdorh5fnbamdbimporc4