Unsupervised dictionary learning with double-layer sparse representation

This paper presents a novel double-layer sparse representation (DLSR) approach for unsupervised dictionary learning. In supervised/unsupervised discriminative dictionary learning, classical approaches usually develop a discriminative term for learning multiple sub-dictionaries, each of which corresponds to one-class training image patches. However, in unsupervised scenario, some of the training patches for learning sub-dictionaries of each class are related to more than one class. Thus, we propose a DLSR formulation, in this paper, to impose the first-layer sparsity on the coefficients and the second-layer sparsity on the classes for each training patch, embedding both the reconstructive (via the first-layer) and discriminative (via the second-layer) abilities in the dictionary. To address the proposed DLSR formulation, a simple yet effective algorithm, called DLSR-OMP, is developed in light of the conventional OMP. Finally, the experimental results show the effectiveness of our approach in the reconstruction task of image denoising and the clustering task of texture segmentation.