非凸低秩稀疏约束的图像超像素分割方法

doi:10.3969/j.issn.1001-2400.2013.05.014

J4 ›› 2013, Vol. 40 ›› Issue (5): 86-91.doi: 10.3969/j.issn.1001-2400.2013.05.014

非凸低秩稀疏约束的图像超像素分割方法

张文娟^1,2;冯象初¹

(1. 西安电子科技大学理学院，陕西西安 710071；
2. 西安工业大学理学院，陕西西安 710012)

收稿日期:2013-01-13 出版日期:2013-10-20 发布日期:2013-11-27
通讯作者: 张文娟
作者简介:张文娟(1980-)，女，讲师，西安电子科技大学博士研究生，E-mail:girl-zwj@163.com．
基金资助:
国家自然科学基金资助项目(61271294, 60872138, 61105011, 11101292)

Image super-pixels segmentation method based on the non-convex low-rank and sparse constraints

ZHANG Wenjuan^1,2;FENG Xiangchu¹

(1. School of Science, Xidian Univ., Xi'an 710071， China;
2. School of Science, Xi'an Technological Univ., Xi'an 710012， China)

Received:2013-01-13 Online:2013-10-20 Published:2013-11-27
Contact: ZHANG Wenjuan

摘要/Abstract

摘要：

将图像超像素分割看作子空间聚类问题．给出一个约束条件，等价于以干净数据为字典．利用系数矩阵的非凸迫近p范数作为稀疏约束，利用系数矩阵奇异值的非凸迫近p范数作为低秩约束，建立非凸极小化模型．运用增广拉格朗日方法和交替极小化方法给出数值计算方法．数值实验表明，笔者提出的约束条件下的分割效果优于原始数据作为字典; 非凸迫近p范数的分割效果优于凸的核范数和l₁范数．

关键词: 图像分割, 超像素, 稀疏, 低秩, 非凸

Abstract:

Image super-pixels segmentation is considered as the subspace clustering problem. A new constraint condition is presented to be equivalent to using the clean data as the dictionary. The non-convex proximal p-norm of the coefficients matrix is used for the sparse constraint, and, the non-convex proximal p-norm of the singular values of the coefficients matrix is used for the low-rank constraint. Then a non-convex minimization model is proposed. The augmented Lagrangian method and the AM (alternating minimization) method are applied for solving the unknown matrices. The results of numerical experiments show that the constraint condition presented in this paper is better than using the original data as the dictionary, and that the non-convex proximal p-norm has a better segmentation result than the convex nuclear norm and l₁ norm.

Key words: image segmentation, super-pixels, sparse, low-rank, non-convex

张文娟;冯象初. 非凸低秩稀疏约束的图像超像素分割方法[J]. J4, 2013, 40(5): 86-91.

ZHANG Wenjuan;FENG Xiangchu. Image super-pixels segmentation method based on the non-convex low-rank and sparse constraints[J]. J4, 2013, 40(5): 86-91.

参考文献

［1］汪云飞, 毕笃彦, 黄飞. 一种用于图像分割的等周算法改进［J］. 西安电子科技大学学报, 2012, 39(2): 87-94.
Wang Yunfei, Bi Duyan, Huang Fei. Improved Isoperimetric Algorithm for Image Segmentation ［J］. Journal of Xidian University, 2012, 39(2): 87-94.
［2］刘建磊, 冯大政. 一种全局最优的非匀质图像分割算法［J］. 西安电子科技大学学报, 2011, 38(2): 66-77.
Liu Jianlei, Feng Dazheng. Non-homogenous Image Segmentation with Global Optimization ［J］. Journal of Xidian University, 2011, 38(2): 66-77.
［3］刘汉强, 焦李成, 赵风. 基于局部相似性测度的SAR图像多层分割［J］. 西安电子科技大学学报, 2010, 37(6): 1071-1076.
Liu Hanqiang, Jiao Licheng, Zhao Feng. SAR Multilevel Segmentation Based on Local Similarity Measure ［J］. Journal of Xidian University, 2010, 37(6): 1071-1076.
［4］ Elhamifar E, Vidal R. Sparse Subspace Clustering ［C］//IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 2790-2797.
［5］ Cheng B, Liu G C, Wang G D, et al. Multi-task Low-rank Affinity Pursuit for Image Segmentation ［C］//International Conference on Computer Vision. Washington: IEEE Computer Society, 2011: 2439-2446.
［6］ Liu G C, Lin Z C, Yan S C, et al. Robust Recovery of Subspace Structures by Low-rank Representation ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 171-184.
［7］ Zhuang L S, Gao H Y, Ma Y, et al. Non-nenagtive Low Rank and Sparse Graph for Semi-supervised Learning ［C］//IEEE Conference on Computer Vision and Pattern Recognition. Providence: IEEE, 2012: 2328-2335.
［8］ Chartrand R, Yin W T. Iteratively Reweighted Algorithms for Compressive Sensing ［C］//IEEE International Conference on Acoustics, Speech, and Signal Processing. Las Vegas: IEEE, 2008: 3869-3872.
［9］ Chartrand R, Staneva V. Restricted Isometry Properties and Nonconvex Compressive Sensing ［J］. Inverse Problems, 2008, 24(3): 1-14.
［10］ Saab R, Chartrand R, Yilmaz O. Stable Sparse Approximations Via Nonconvex Optimization ［C］//IEEE International Conference on Acoustics, Speech, and Signal Processing. Las Vegas: IEEE, 2008: 3885-3888.
［11］ Chartrand R. Nonconvex Splitting for Regularized Low-rank+sparse Decomposition ［J］. IEEE Transactions on Signal Processing, 2012, 60(11): 5810 - 5819.
［12］ Mori G, Ren X F, Efros A A, et al. Recovering Human Body Configurations: Combining Segmentation And Recognition ［C］//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition: 2. Washington: IEEE, 2004: II-326-333.
［13］ Wei S M, Lin Z C. Analysis and Improvement of Low Rank Representation for Subspace Segmentation ［R/OL］. ［2011-07-10］. http://arxiv.org/abs/1107.1561v1.
［14］ William M R. Objective Criteria for the Evaluation of Clustering Methods ［J］. Journal of the American Statistical Association, 1971, 66(336): 846-850.
［15］ Marina M. Comparing Clustering: an Axiomatic View ［C］//Proceedings of the 22nd International Conference on Machine Learning. New York: ACM, 2005: 577-584.
［16］ Arbelaez P, Maire M, Fowlkes C, et al. From Contour to Regions: an Empirical Evaluation ［C］//IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 2294-2301.

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非凸低秩稀疏约束的图像超像素分割方法

Image super-pixels segmentation method based on the non-convex low-rank and sparse constraints

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