Discriminative dictionary pair learning based on differentiable support vector function for visual recognition

Sparse representation and discriminative dictionary learning (DDL) algorithm has become a widely-used model in visual recognition systems, and various discrimination terms are introduced into the DDL models to enhance the discriminative ability and the recognition rate. Recently, an algorithm named dictionary pair learning (DPL) was proposed which jointly learned a synthesis dictionary and an analysis dictionary to promote the recognition performance. In this paper, a novel dictionary learning model is proposed which introduces a differentiable support vector discriminative term into the original DPL model. In the dictionary learning stage, the proposed model can jointly train a synthesis dictionary, an analysis dictionary and a support vector discriminative term. In the classification stage, the class label is decided by the joint effect of the reconstruction residual, the projective discrimination term and the support vector function. Experimental results on various image recognition benchmarks such as face recognition, scene categorization and object classification are presented to demonstrate the effectiveness of the proposed method.