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.
|Published - Jul 2010
|CIVR 2010 - ACM International Conference on Image and Video Retrieval - Xi'an, China
Duration: 1 Jul 2010 → …
|CIVR 2010 - ACM International Conference on Image and Video Retrieval
|1/07/10 → …