  In a sparse representation based recognition scheme, it is critical to learn
a desired dictionary, aiming both good representational power and
discriminative performance. In this paper, we propose a new dictionary learning
model for recognition applications, in which three strategies are adopted to
achieve these two objectives simultaneously. First, a block-diagonal constraint
is introduced into the model to eliminate the correlation between classes and
enhance the discriminative performance. Second, a low-rank term is adopted to
model the coherence within classes for refining the sparse representation of
each class. Finally, instead of using the conventional over-complete
dictionary, a specific dictionary constructed from the linear combination of
the training samples is proposed to enhance the representational power of the
dictionary and to improve the robustness of the sparse representation model.
The proposed method is tested on several public datasets. The experimental
results show the method outperforms most state-of-the-art methods.
