A Bayesian Approach for Joint Discriminative Dictionary and Classifier Learning

Sparse representation has been widely applied to image classification, where the key issue is to extract a suitable discriminative dictionary. To this end, we propose a joint dictionary and classifier learning algorithm based on a parameterized Bayesian model. Therein, the Gaussian priors of a dictionary endow it with the capability of discrimination and representation. Moreover, we introduce a multivariate Gaussian prior for the sparse codes to achieve group sparsity, thereby substantially improving the classification performance. Furthermore, the sparse codes are estimated by a group-sparse Bayesian learning (GSBL) method, and the dictionary atoms are updated sequentially by maximizing a posterior. Moreover, to avoid manual parameter adjustment, the hyperparameters are optimized by an evidence maximization method. Accordingly, we develop a classification scheme via GSBL. Finally, extensive experiments are conducted on six benchmark datasets of face classification, object recognition, handwritten recognition, and scene categorization to substantiate the effectiveness and superiority of the proposed method.

Automated summary

This paper proposes a joint dictionary and classifier learning algorithm that optimizes dictionaries and sparse codes using Gaussian priors to improve classification performance. The algorithm achieves group sparsity in sparse coding through Bayesian learning and Gaussian priors. Hyperparameters are optimized using an evidence maximization method without manual parameter tuning.