Simplified Structured Analysis Dictionary Learning for Image Classification

In this paper, we focus on image classification based on dictionary learning. The structured analysis dictionary learning (SADL) algorithm introduces a mapping matrix to the representation coefficient matrix and imposes a structural constraint to enhance the capability of discrimination, and it achieves promising classification results. However, in the formulation of SADL, the discrimination of the model is mainly based on the transformed coefficient matrix rather than the original coefficients, and thus the sparsity of the original coefficients has little impact on classification. In addition, classification is based on a linear classifier learned simultaneously with the dictionary, which may also restrict the performance of the algorithm. To address these issues, we propose a simplified SADL (SSADL) algorithm by simplifying the original formulation of SADL and introducing a new classification approach based on support vector machine (SVM). Simulation results on widely used databases demonstrate that the proposed SSADL algorithm achieves better performance than several state-of-the-art classification algorithms based on dictionary learning.