  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.
