CVPR 2011 (2011)
June 20, 2011 to June 25, 2011
Jianping Shi , Dept. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Xiang Ren , Dept. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Guang Dai , Dept. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Zhihua Zhang , Dept. of Comput. Sci. & Technol., Zhejiang Univ., Hangzhou, China
Dictionary learning is a challenging theme in computer vision. The basic goal is to learn a sparse representation from an overcomplete basis set. Most existing approaches employ a convex relaxation scheme to tackle this challenge due to the strong ability of convexity in computation and theoretical analysis. In this paper we propose a non-convex online approach for dictionary learning. To achieve the sparseness, our approach treats a so-called minimax concave (MC) penalty as a nonconvex relaxation of the $/ell_0)$ penalty. This treatment expects to obtain a more robust and sparse representation than existing convex approaches. In addition, we employ an online algorithm to adaptively learn the dictionary, which makes the non-convex formulation computationally feasible. Experimental results on the sparseness comparison and the applications in image denoising and image inpainting demonstrate that our approach is more effective and flexible.
image inpainting, nonconvex relaxation, sparse dictionary learning, computer vision, sparse representation, overcomplete basis set, nonconvex online approach, minimax concave penalty, image denoising
Jingdong Wang, Jianping Shi, Guang Dai, Xiang Ren and Zhihua Zhang, "A non-convex relaxation approach to sparse dictionary learning," CVPR 2011(CVPR), Providence, RI, 2011, pp. 1809-1816.