A New Approach for Learning Discriminative Dictionary for Pattern Classification

Dictionary learning (DL) for sparse coding based classification has been widely researched in pattern recognition in recent years. Most of the DL approaches focused on the reconstruction performance and the discriminative capability of the learned dictionary. This paper proposes a new method for learning discriminative dictionary for sparse representation based classification, called Incoherent Fisher Discrimination Dictionary Learning (IFDDL). IFDDL combines the Fisher Discrimination Dictionary Learning (FDDL) method, which learns a structured dictionary where the class labels and the discrimination criterion are exploited, and the Incoherent Dictionary Learning (IDL) method, which learns a dictionary where the mutual incoherence between pairs of atoms is exploited. In the combination, instead of considering the incoherence between atoms in a single shared dictionary as in IDL, we propose to incorporate the incoherence between pairs of atoms within each sub-dictionary, which represent a specific object class. This aims to increase discrimination capacity of between basic atoms in sub-dictionaries. The combination allows one to exploit the advantages of both methods and the discrimination capacity of the entire dictionary. Extensive experiments have been conducted on benchmark image data sets for Face recognition (ORL database, Extended Yale B database, AR database) and Digit recognition (the USPS database). The experimental results show that our proposed method outperforms most of state-of-the-art methods for sparse coding and DL based classification, meanwhile maintaining similar complexity.