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Sparse Dictionary-based Representation and Recognition of Action Attributes
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BibTeX
@MISC{Qiu_sparsedictionary-based,
author = {Qiang Qiu and Zhuolin Jiang},
title = {Sparse Dictionary-based Representation and Recognition of Action Attributes},
year = {}
}
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Abstract
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 item. We propose a Gaussian Process (GP) model for sparse representation to optimize the dictionary objective function. The sparse coding property allows a kernel with a 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 applications. 1.
Keyphrases
action attribute sparse dictionary-based representation appearance information objective function class distribution gaussian process modeled action category action video unmodeled action category dictionary objective function sparse feature space dictionary learning discriminative action compact support sparse dictionary efficient dictionary learning process sparse coding property information maximization mutual information sparse representation action recognition application experimental result dictionary item
