Relaxed support vector based dictionary learning for image classification

Discriminative dictionary learning (DDL) has attracted significant attention in the field of image classification. To enhance the classification performance, most existing discriminative dictionary learning methods introduce supervision information on the dictionary to project raw training samples into a coefficient subspace. However, the strict constraint on coefficient features may not conducive to the separation of the training samples from different classes for dictionary learning. In this paper, we propose Relaxed Support Vector based Dictionary Learning (RSVDL) for image recognition, which can efficiently learn coefficient features with powerful discrimination and representation capabilities. By constructing a relaxed coefficient subspace that is closely associated with label information, the discriminative of the learned dictionary is also improved. Experimental results on several benchmark datasets show that the proposed RSVDL method is very effective for various image classification tasks. Moreover, the experiments on more challenging datasets further reveal the state-of-art performance of our method by using with the CNN features.