Learning dictionary via subspace segmentation for sparse representation

Sparse signal representation based on redundant dictionaries contributed to much progress in image processing in the past decades. But the common overcomplete dictionary model is not well structured and there is still no guideline for selecting the proper dictionary size. In this paper, we propose a new algorithm for dictionary learning based on subspace segmentation. Our algorithm divides the training data into sub-spaces and constructs the dictionary by extracting the shared basis from multiple subspaces. The learned dictionary is well structured and its size is adaptive to the training data. We analyze this algorithm and demonstrate its ability on some initial supportive experiments using real image data.