Robust Image Classification via Low-Rank Double Dictionary Learning

In recent years, dictionary learning has been widely used in various image classification applications. However, how to construct an effective dictionary for robust image classification task, in which both the training and the testing image samples are corrupted, is still an open problem. To address this, we propose a novel low-rank double dictionary learning (LRD²L) method. Unlike traditional dictionary learning methods, LRD²L simultaneously learns three components from training data: (1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative features owned by each class, (2) a low-rank class-shared dictionary which models the common patterns shared by different classes and (3) a sparse error container to fit the noises in data. As a result, the class-specific information, the class-shared information and the noises contained in data are separated from each other. Therefore, the dictionaries learned by LRD²L are noiseless, and the class-specific sub-dictionary of each class can be more discriminative. Also since the common features across different classes, which are essential to the reconstruction of image samples, are preserved in class-shared dictionary, LRD²L has a powerful reconstructive capability for newly coming testing samples. Experimental results on three public available datasets reveal the effectiveness and the superiority of our approach compared to the state-of-the-art dictionary learning methods.