Sparse Dictionary-based Attributes for Action Recognition and Summarization
This work addresses action recognition and summarization in videos, which is an incremental improvement for computer vision applications.
The authors tackled action recognition and summarization by learning a sparse dictionary of action attributes through information maximization, achieving effective results in recognizing both modeled and unseen action categories.
We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective function for learning a sparse dictionary of action attributes. The objective function maximizes the mutual information between what has been learned and what remains to be learned in terms of appearance information and class distribution for each dictionary atom. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with compact support in GP to realize a very efficient dictionary learning process. Hence we can describe an action video by a set of compact and discriminative action attributes. More importantly, we can recognize modeled action categories in a sparse feature space, which can be generalized to unseen and unmodeled action categories. Experimental results demonstrate the effectiveness of our approach in action recognition and summarization.