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Portable high-quality sports cameras (e.g. head or helmet mounted) built for recording dynamic first-person video footage are becoming a common item among many sports enthusiasts. We address the novel task of discovering firstperson action categories (which we call ego-actions) which can be useful for such tasks as video indexing and retrieval. In order to learn ego-action categories, we investigate the use of motion-based histograms and unsupervised learning algorithms to quickly cluster videodoi:10.1109/cvpr.2011.5995406 dblp:conf/cvpr/KitaniOSS11 fatcat:hf24ho4g7jfm3nxddavtf52s7i