Structural Constraint and Discriminative Constraint Based Dictionary Pair Learning for Image Classification

The dictionary learning algorithm is an effective image classification algorithm. To tackle with the problems including noise, occlusion, small sample problem, and so on, many discriminative dictionary learning models have been proposed. However, the performance of these algorithms might not be satisfactory, the main reason is that some useful features are not effectively disclosed. For example, the coding coefficients of the samples have row sparsity consistency and the block structure property. Maintaining the inherent features of the samples (e.g. the block structure, local characteristics of atoms) is beneficial to enhance the discriminative ability of the model. In addition, a powerful discriminative constraint is also essential to improve the performance of image classification. In the paper, we propose a new structural constraint and discriminative constraint based dictionary pair learning for image classification. In the model, the L21 norm constraint is applied on the analysis sub-dictionary to ensure the row sparsity consistency of the coding coefficients as much as possible. A new discriminative constraint is designed to enforce the representation matrix to be more discriminative, which can be acquired by minimizing the intra-class scatter and maximizing the inter-class scatter of the samples. Besides, we define a new atomic locality constraint, which forces the atoms to preserve the structure information of the samples. Finally, seven benchmark data sets are selected to evaluate the performance of the proposed method in comparison with popular methods. The experimental results outperform the state-of-the-art methods, which demonstrates the efficacy of the proposed model.