Multi-layer discriminative dictionary learning with locality constraint for image classification

Abstract

Discriminative dictionary learning (DDL) has demonstrated significantly improved performance for image classification. However, most of the existing DDL methods just adopt the single-layer dictionary learning architecture, which narrows the discriminative ability of the coding vectors. Another limitation of these methods is that the atoms of the learned dictionary are easily affected by the noise in the original data. To this end, a powerful architecture, called the multi-layer discriminative dictionary learning (MDDL) with locality constraint, is proposed for image classification. Through the multi-layer dictionary learning, the robust dictionary is obtained in the final layer, where the separability of coding vectors from different classes is also increased. Meanwhile, benefiting from joint classifier training and multi-layer dictionary learning, the discriminability of the learned coding vectors is further enhanced. Besides, by utilizing the graph Laplacian matrices based on the learned dictionaries, not only the locality information of the original data is preserved, but also it can avoid very large values in the coding vectors to reduce the test error caused by overfitting. In addition, an iterative algorithm is devised to efficiently solve the proposed MDDL. The experimental results demonstrate that our methods can achieve promising classification results on well-known benchmark image datasets.