Sparse and Low-Rank Coupling Image Segmentation Model Via Nonconvex Regularization

Author:

Zhang, Xiujun   Xu, Chen   Li, Min   Sun, Xiaoli  

Journal:

International Journal of Pattern Recognition and Artificial Intelligence

Issue Date:

2015

Abstract(summary):

This paper investigates how to boost region-based image segmentation by inheriting the advantages of sparse representation and low-rank representation. A novel image segmentation model, called nonconvex regularization based sparse and low-rank coupling model, is presented for such a purpose. We aim at finding the optimal solution which is provided with sparse and low-rank simultaneously. This is achieved by relaxing sparse representation problem as L-1/2 norm minimization other than the L-1 norm minimization, while relaxing low-rank representation problem as the S-1/2 norm minimization other than the nuclear norm minimization. This coupled model can be solved efficiently through the Augmented Lagrange Multiplier (ALM) method and half-threshold operator. Compared to the other state-of-the-art methods, the new method is better at capturing the global structure of the whole data, the robustness is better and the segmentation accuracy is also competitive. Experiments on two public image segmentation databases well validate the superiority of our method.

Page:

1555004