A novel multi-layer discriminative dictionary learning approach for image classification

Abstract

Discriminative dictionary learning (DDL) has been confirmed to be effective for image classification. However, existing DDL approaches often fail to extract deep hierarchical information due to the single-layer dictionary learning framework. Moreover, they overlook the atoms-label information in the dictionary, leading to reduced feature distinctiveness and lower classification accuracy. To overcome the above problem, a novel DDL method, called the Multi-layer local constraint and label embedding dictionary learning (M-LCLEDL), is proposed. Specifically, the novel multi-layer DDL framework, which is formed by stacking the DL process one by one, is designed to learn the deep hierarchical and nonlinear features. The layer-by-layer stacking of the DL process in the multi-layer DDL framework allows for the elimination of redundant and interfering features. This step-by-step elimination process enhances the stability and robustness of the framework. Additionally, to leverage the label information carried by the labeled training samples, atoms with label embedding and locality structure are introduced. The proposed approach includes a fast iteration strategy for efficient optimization. Experimental results demonstrate that the approach is relatively insensitive to dictionary size, achieving promising performance and greater stability compared to most DDL-based image classification approaches.