Dictionary learning based on discriminative energy contribution for image classification

This paper combines the discriminative feature extraction and effective classifier construction into a single framework to learn a structured discriminative dictionary for image classification. Due to the fact that the discriminative signal lie in a low dimensional subspace and can be well represented only via a few atoms of the learned dictionary, this paper addresses the feature extraction via learning a dictionary, whose sub dictionaries preserve correspondence to the class labels, and an optimal linear classifier jointly based on the structure of energy contribution. Based on the discriminative energy contributions, we are searching the discriminative feature for classification rather than reconstructing the data accurately. In addition, with the assumption that the classifier has a specific property which is similar with the dictionary, we learn a classifier to make the dictionary optimal and have a low cost on classifying. Experiment results on the several databases to specific classification tasks are conducted to verify the efficacy of the proposed method compared with the state-of-the-art dictionary learning for classification methods.