基于自适应CPSO算法的二维模糊熵图像阈值分割

计算机科学 ›› 2013, Vol. 40 ›› Issue (5): 296-299.

基于自适应CPSO算法的二维模糊熵图像阈值分割

赵越,李晶皎,徐鑫,陈超,白鑫

渤海大学计算机教研部锦州121013;东北大学信息科学与工程学院沈阳110819;渤海大学计算机教研部锦州121013;渤海大学计算机教研部锦州121013;东北大学信息科学与工程学院沈阳110819

出版日期:2018-11-16 发布日期:2018-11-16
基金资助:
本文受国家自然科学基金项目(60970157)资助

2D Fuzzy Entropy Image Threshold Segmentation Method Based on CPSO

ZHAO Yue,LI Jing-jiao,XU Xin,CHEN Chao and BAI Xin

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 由于PSO算法会出现虚假收敛或者早熟等现象,提出了一种自适应混沌粒子群算法(ACPSO)及其在图像分割中的应用。首先提出了一种改进的自适应粒子群优化算法(IAPSO)。然后在IAPSO的基础上,加入了混沌优化方法,用混沌变量来初始化粒子的位置和速度,并用新的无限折叠混沌映射对算法进行混沌变异,从当前群体中择优选择部分粒子进行混沌优化。最后将ACPSO算法应用到图像分割中。通过与最大模糊Shannon熵阈值分割法、基于基本PSO的最大模糊Shannon熵阈值分割法进行对比,验证了基于自适应CPSO算法的二维模糊熵阈值图像分割方法的性能更好。

关键词: CPSO算法,自适应,混沌粒子群,二维图像分割

Abstract: Because PSO algorithm is occurrence of false convergence or precocious,an adaptive chaotic particle swarm optimization(ACPSO) and its application to image segmentation were proposed．First,an improved adaptive particle swarm optimization algorithm(IAPSO) was proposed．Second,the chaos optimization algorithm was joined into the IAPSO.The chaotic variable was used to initialize the position and velocity of the particles．The new Infinite Collapses chaos chaotic mutation mapping algorithm was used to choose the best part of particles from the current population chaos optimization．Finally,ACPSO algorithm was applied to image segmentation．The maximum fuzzy Shannon entropy threshold segmentation method was compared with maximum fuzzy Shannon entropy threshold segmentation method based on the basic PSO.The result indicates that the fuzzy entropy threshold image segmentation method based on the algorithm for the adaptive CPSO has better performance.

Key words: CPSO algorithm,Adaptive,Chaos-particle swarm,2D image segmentation

赵越,李晶皎,徐鑫,陈超,白鑫. 基于自适应CPSO算法的二维模糊熵图像阈值分割[J]. 计算机科学, 2013, 40(5): 296-299. https://doi.org/

ZHAO Yue,LI Jing-jiao,XU Xin,CHEN Chao and BAI Xin. 2D Fuzzy Entropy Image Threshold Segmentation Method Based on CPSO[J]. Computer Science, 2013, 40(5): 296-299. https://doi.org/

参考文献

[1] 邹小林,陈伟福,冯国灿.基于谱聚类的多阈值图像分割方法[J].计算机科学,2012,9(3):246-248
[2] 曹铮.模糊聚类及其在图像分割中的应用[D].保安:河北大学,2010
[3] Sathya P D,Kayalvizhi R.Modified bacterial foraging algorithm based multilevel thresholding for image segmentation [J].Engineering Applications of Artificial Intelligence,2011,4(4):595-615
[4] 吴薇.基于最大模糊熵原理的多阈值图像分割新算法[J].系统工程与电子学报,2005,8(2):64-68
[5] Kennedy J,Eberhart R C.Particle Swarm Optimization:Neural Networks [C]∥IEEE International Conference.NJ:Piscata-way,1995:1942-1948
[6] Chakraborty S,Senjyu T,Saber A Y,et al．A novel particleswarm optimization method based on quantum mechanics computation for thermal economic load dispatch problem [J]．IEEJ Transactions on Electrical and Electronic Engineering,2012,7(5):461-470
[7] Lienhart R,Effelsberg W.Automatic text segmentation and text recogni- tion for video indexing [J].Multimedia System,2000,8(1):69-81
[8] 田杰,曾建潮.基于QPSO的二维模糊最大熵图像阈值分割方法[J].计算机工程,2009,5(3):230-232
[9] Ghosh S,Das S,Kundu D,et al．An inertia-adaptive particleswarm system with particle mobility factor for improved global Optimization [J].Neural Comput & Applic,2012,21:237-250
[10] Liu Bo,Wang Ling,Jin Yi-hui,et al.Improved particle swarmoptimiza- tion combined with chaos[J].Chaos Solitons & Fractals,2005,25(5):1261-1271
[11] Jiang Hui-min,Kwong C K,Chen Zeng-qiang,et al．Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control [J]．Expert Systems with Applications,2012,39(1):194-201
[12] 孙越泓．基于粒子群优化算法的图像分割研究[D]．南京:南京理工大学,2010
[13] 金立左,袁晓辉.二维模糊划分最大嫡图像分割算法[J].电子与信息学报,2002,4(8):1040-1044
[14] Zhao M,Fu A M N,Yan H．A technique of three-level thresholding based on probability partition and fuzzy 3-partition [J]．IEEE Transactions on Fuzzy Systems,2001,9(3):469-479

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