Tucker decomposition-based tensor learning for human action recognition

The spatial information is the important cue for human action recognition. Different from the vector representation, the spatial structure of human action in the still images can be preserved by the tensor representation. This paper proposes a robust human action recognition algorithm by tensor representation and Tucker decomposition. In this method, the still image containing human action is represented by a tensor descriptor (Histograms of Oriented Gradients). This representation preserves the spatial information inside the human action. Based on this representation, the unknown tensor parameter is decomposed according to the Tucker tensor decomposition at first, and then the optimization problems can be solved using the alternative optimization method, where at each iteration, the tensor descriptor is projected along one order and the parameter along the corresponding order can be estimated by solving the Ridge Regression problem. The estimated tensor parameter is more discriminative because of effectively using the spacial information along each order. Experiments are conducted using action images from three publicly available databases. Experimental results demonstrate that our method outperforms other methods.