Class relatedness oriented-discriminative dictionary learning for multiclass image classification

Dictionary learning (DL) has recently attracted intensive attention due to its representative and discriminative power in various classification tasks. Although much progress has been reported in the existing supervised DL approaches, it is still an open problem that how to build the relationship between dictionary atoms and the class labels in multiclass classification. In this paper, based on the assumption that the relevance of dictionary atoms could be helpful in multiclass classification task, we proposed a class relatedness oriented (CRO) discriminative dictionary learning method for sparse coding. Utilizing the ℓ1,∞-norm regularization on the coding coefficient matrix, the proposed method can adaptively learn the class relatedness between dictionary atoms and the multiclass labels. Experimental results of face recognition, object classification, and action recognition demonstrate that our proposed method is comparable to many state-of-the-art DDL methods.