A set of co-occurrence matrices on the intrinsic manifold of human silhouettes for action recognition

Authors:
Feng Zheng;Ling Shao;Zhan Song
Affiliations:
Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, Hong Kong, China;University of Sheffield, UK;Chinese Academy of Sciences, Shenzhen, China and The Chinese University of Hong Kong, Hong Kong, China
Venue:
Proceedings of the ACM International Conference on Image and Video Retrieval
Year:
2010

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Abstract

Recognizing actions from a monocular video is a very hot topic in computer vision recently. In this paper, we propose a new representation of actions on the intrinsic shape manifold learned by various graph embedding algorithms. The co-occurrence matrices descriptor captures more temporal information than the histogram descriptor which only considers the spatial information. In addition, we compare the performance of the co-occurrence matrices descriptor on different manifolds learned by various graph embedding methods. The results show that nonlinear algorithms are more robust than linear ones. Furthermore, we conclude that label information plays a critical role in learning more discriminating manifolds.