Sparse and Low-Rank Coupling Image Segmentation Model Via Nonconvex Regularization
- 27 February 2015
- journal article
- Published by World Scientific Pub Co Pte Ltd in International Journal of Pattern Recognition and Artificial Intelligence
- Vol. 29 (2), 1555004
- https://doi.org/10.1142/s0218001415550046
Abstract
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 L1/2 norm minimization other than the L1 norm minimization, while relaxing low-rank representation problem as the S1/2 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.Keywords
This publication has 17 references indexed in Scilit:
- Robust Recovery of Subspace Structures by Low-Rank RepresentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Interactive Image Segmentation Using Dirichlet Process Multiple-View LearningIEEE Transactions on Image Processing, 2011
- A Multiphase Image Segmentation Method Based on Fuzzy Region CompetitionSIAM Journal on Imaging Sciences, 2010
- Lower Bound Theory of Nonzero Entries in Solutions of $\ell_2$-$\ell_p$ MinimizationSIAM Journal on Scientific Computing, 2010
- Exact Matrix Completion via Convex OptimizationFoundations of Computational Mathematics, 2009
- Spectral Curvature Clustering (SCC)International Journal of Computer Vision, 2008
- For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solutionCommunications on Pure and Applied Mathematics, 2006
- Efficient Graph-Based Image SegmentationInternational Journal of Computer Vision, 2004
- Mean shift: a robust approach toward feature space analysisIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Objective Criteria for the Evaluation of Clustering MethodsJournal of the American Statistical Association, 1971