Discriminant sparse coding for image classification

Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is not the optimum dictionary for the classification task. In this paper, based on the global image statistics, a novel discriminant dictionary learning method combining linear discriminant analysis with sparse coding is proposed to obtain a more discriminative dictionary while preserving its descriptive abilities and a block coordinate descent algorithm is proposed to solve the optimization problem. Experimental results show that our algorithm has capabilities to learn dictionary with more discriminative power and achieves superior performance.