Abstract

In this paper, a block sparse discriminative classification framework (BSDC) is proposed under the assumption that a block or group structure exists in sparse coefficients on classification. First, we propose a block discriminative dictionary-learning (BDDL) algorithm, which learns class-specific subdictionaries and forces the sparse coefficients to be block sparse. An efficient gradient-based optimization strategy of BDDL also is developed, and the block sparse constraint of the sparse coefficient leads to a least-squares solution of nonzero entries in the sparse coding stage of dictionary learning. Second, to take advantage of the structures when a new test sample is given, conventional sparse coding algorithms are discarded, and structured sparse coding methods are adopted. Experiments validate the effectiveness of the proposed framework in face recognition and texture classification. We also show that BSDC is robust to noise.

© 2014 Optical Society of America

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