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

Jianqiang Song, Xuemei Xie, Guangming Shi, Weisheng Dong

Abstract: Highlights•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.•Benefiting from joint classifier training and multi-layer dictionary learning, the discriminability of the learned coding vectors is further enhanced.•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.