Multi-view Discriminant Dictionary Learning via Learning View-specific and Shared Structured Dictionaries for Image Classification

Recently, multi-view dictionary learning technique has attracted lots of research interest. Although some multi-view dictionary learning methods have been addressed, there exists much room for improvement. How to explore and utilize both the diversity and the useful correlation information of different views with dictionaries has not been well studied. In this paper, we propose a novel multi-view dictionary learning approach named multi-view discriminant dictionary learning via learning view-specific and shared structured dictionaries (MDVSD), which aims to learn a structured dictionary shared by all views and multiple view-specific structured dictionaries with each corresponding to a specific view. The shared dictionary is combined with each view-specific dictionary to represent data of the specific view. MDVSD makes the view-specific dictionaries corresponding to different views uncorrelated for effectively exploring the diversity of different views. Furthermore, we introduce structural uncorrelation into shared dictionary learning procedure, such that the useful correlation information of different views can be effectively exploited. Dictionary-atoms in shared and view-specific dictionaries have correspondence to class labels so that the learned dictionaries have favorable discriminant ability and the obtained reconstruction error is discriminative. Three widely used datasets are employed as test data. Experimental results demonstrate the effectiveness of the proposed approach.