基于量子粒子群的广义模糊熵阈值法参数选取

Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (10): 2492-2496.

基于量子粒子群的广义模糊熵阈值法参数选取

雷博¹, 范九伦²

1. 西安电子科技大学电子工程学院, 陕西, 西安, 710071;
2. 西安邮电学院信息与控制系, 陕西, 西安, 710061

收稿日期:2008-02-20 修回日期:2008-12-01 出版日期:2009-10-20 发布日期:2010-01-03
作者简介:雷博(1981- ),女,讲师,博士,主要研究方向为模式识别、图像分割.E-mail:leileibo@xiyou.edu.cn
基金资助:
国家自然科学基金(60572133);陕西省教育厅专项科研计划(09JK721)资助课题

Preferences for generalized fuzzy entropy-based thresholding method based on quantum-behavior particle swarm optimization

LEI Bo¹, FAN Jiu-lun²

1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
2. Dept. of Information and Control, Xi'an Inst. of Posts and Telecommunications, Xi'an 710061, China

Received:2008-02-20 Revised:2008-12-01 Online:2009-10-20 Published:2010-01-03

摘要/Abstract

摘要： 针对广义模糊熵阈值分割法中参数m的选择问题,提出了一种结合优化算法的自适应参数选取算法.该算法依据一种图像分割质量评价指标建立目标函数,再基于量子粒子群优化搜索算法在参数的变化空间自适应地搜索最佳参数,同时依据模糊熵最大准则对S型隶属度函数中的三个参数(a,b,d)进行了全局组合寻优,从而建立了一个嵌套的优化搜索过程,实现了广义模糊熵图像阈值分割方法的自动阈值选取.实验表明,该方法对光照不均匀图像有更好的分割效果.

Abstract: An adaptive preferences method is proposed to find the parameter m in the generalized fuzzy entropy with optimization algorithm.The novel method builds an objective function based on an image segmentation quality evaluation criterion for searching the optimal parameter of the generalized fuzzy entropy and builds the other objective function based on the maximum fuzzy entropy criterion for searching the membership function parameters(a,b,d) in their variation space with the quantum-behavior particle swarm optimization(QPSO) algorithm,respectively.Therefore,it is a nest searching process and realizes the aim of searching the threshold in generalized fuzzy entropy-based image segmentation method automatically.Experiment results show that the new method can obtain good segmentation results on images with illumination noises.

中图分类号:

TN911.73

雷博, 范九伦. 基于量子粒子群的广义模糊熵阈值法参数选取[J]. Journal of Systems Engineering and Electronics, 2009, 31(10): 2492-2496.

LEI Bo, FAN Jiu-lun. Preferences for generalized fuzzy entropy-based thresholding method based on quantum-behavior particle swarm optimization[J]. Journal of Systems Engineering and Electronics, 2009, 31(10): 2492-2496.

参考文献

[1] Pal S K,King R A,HaShim A A.Automatic greylevel thresholding through index of fuzziness and entropy[J].Pattern Recognition Letter,1983,1(3):141-146.
[2] Murthy C A,Pal S K.Histogram thresholding by minimizing graylevel fuzziness[J].Information Sciences,1992,60(1-2):107-135.
[3] Li X Q,Zhao Z W,Cheng H D.Fuzzy entropy threshold approach to cancer detection[J].Information Sciences,1995,4(1):49-56.
[4] Cheng H D,Chen J R,Li J.Thresholding selection based on fuzzy c-partition entropy approach[J].Pattern Recognition,1998,31(7):857-870.
[5] Tao W B,Tian J W,Liu J.Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm[J].Pattern Recognition Letters,2003,24(16):3069-3078.
[6] 陶文兵,田金文,柳健,等.基于遗传算法和模糊熵的前视红外图像分割[J].红外与毫米波学报,2003,22(6):465-468.
[7] Zadeh L A.Fuzzy sets[J].Information and Control,1965,8(3):338-353.
[8] Fan J L,Zhao F.A generalized fuzzy entropy-based image segmentation method[C]//Proc.of the International Conference on Intelligent Systems and Knowledge Engineering,2007:427-431.
[9] Zenzo S D,Cinque L,Levialdi S.Image thresholding using fuzzy entropies[J].IEEE Trans.on Systems,Man and Cybernetics:Part B,1998,28(1):15-23.
[10] 范九伦,赵凤.基于Sugeno补的广义模糊熵阈值分割方法[J].电子与信息学报,2008,30(8):1865-1868.
[11] 支晓斌,范九伦.一种广义模糊补运算和相应的广义模糊熵[J].模糊系统与数学,2008,22(1):96-102.
[12] Sugeno M,Fuzzy measures and fuzzy integrals;a survey[C]//Gupta M,Saridis G N,Gaines B R.Fuzzy Automata and Decision Processes,1977:89-102.
[13] Andrew K C W,Sahoo P K.A gray-level selection method based on maximum entropy principle[J].IEEE Trans.on Systerns,Man and Cybernetics,1989,19(4):866-871.
[14] Eberhart R C,Kermedy J.A new optimizer using particles swarm theory[C]//Proc.of 6th International Symposium on Micro Machine and Human Science,1995:39-43.
[15] Sun J,Xu W B,Feng B.A global search strategy of quantumbahaved panicle swarm optimization[C]//Proc.of IEEE Con ference on Cybernetics and Intelligent Systems,2004:111-116.