Action Recognition Using Graph Embedding and the Co-occurrence Matrices Descriptor
Résumé
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, the co occurrence matrices de-scriptor, on the intrinsic shape manifold learned by graph embedding. The co-occurrencematrices descriptor captures more temporal information than the bag of words (histogram) descriptor which only considers the spatial information, thus boosts the classi¯cation accuracy. In addition, we compare the performance of the co-occurrence matrices descriptor on different manifolds learned by various graph embedding methods. Graph embedding methods preserve as much of the signi¯cant structure of the high-dimensional data as possible in the low-dimensional map. The results show that nonlinear algorithms are more robust than linear ones. Furthermore, we conclude that the label information plays a critical role in learning more discriminating manifolds.
Domaines
Informatique [cs]
Origine : Fichiers produits par l'(les) auteur(s)
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