基于动态粒子群优化与K-means聚类的图像分割算法Image segmentation algorithm based on dynamic particle swarm optimization and K-means clustering
李立军,张晓光
摘要(Abstract)：
为了解决K-means聚类算法图像分割质量过度依赖于初始聚类中心选取,且易于陷入局部最优解等问题,提出一种基于动态粒子群优化(DPSO)与K-means聚类的图像分割算法(DPSOK)。通过动态调整惯性系数与学习因子来增强PSO算法的性能;然后计算粒子群适应度方差,找准切换至K-means算法时机;随后,将DPSO输出结果用来初始化K-means聚类中心,使其收敛至全局最优解;最后,通过最小化目标函数的多次迭代,使K-means的聚类中心不断更新,直到收敛。实验结果表明,DPSOK能有效提高K-means的全局搜索能力,在图像分割中它比K-means,PSO获得了更好的分割效果,且与粒子群优化和K-means算法相比,DPSOK算法具有更高的分割质量与效率。
关键词(KeyWords)： 图像分割;动态粒子群优化;K-means聚类;适应度方差;聚类算法;DPSOK
基金项目(Foundation): 国家自然科学基金项目(51274202);; 教育部第六批国家特色专业建设项目(TS1Z293);; 江苏省自然科学基金项目(BK20130199)~~
作者(Author): 李立军,张晓光
DOI: 10.16652/j.issn.1004-373x.2018.10.042
参考文献(References)：
- [1]吉长东,李相泽,敖国政.基于全局K-means算法的超像素分割方法[J].沈阳大学学报(自然科学版),2017,29(3):212-216.JI Changdong,LI Xiangze,AO Guozheng.Superpixel segmentation method based on global K-means[J].Journal of Shenyang University(Natural science),2017,29(3):212-216.
- [2]伍育红.聚类算法综述[J].计算机科学,2015,42(z1):491-499.WU Yuhong.General overview on clustering algorithms[J].Computer science,2015,42(S1):491-499.
- [3]MEDEIROS R S,SCHARCANSKI J,WONG A.Image segmentation via multi-scale stochastic regional texture appearance models[J].Computer vision and image understanding,2016,142(C):23-36.
- [4]FENG Yuncong,ZHAO Haiying,LI Xiongfei.,et al A multiscale 3D Otsu thresholding algorithm for medical image segmentation[J].Digital signal processing,2016,60:186-199.
- [5]CHEN Na,XU Zeshui,XIA Meimei.Hierarchical hesitant fuzzy K-means clustering algorithm[J].Applied mathematics:a journal of Chinese universities,2014,29(1):1-17.
- [6]穆瑞辉,苗国义.基于粒子群优化的模糊K-means目标分类算法[J].计算机测量与控制,2013,21(5):1266-1268.MU Ruihui,MIAO Guoyi.Algorithm for goal classification based on particle swarm optimization and fuzzy K-means[J].Computer measurement&control,2013,21(5):1266-1268.
- [7]SIDDIQUI F U,ISA N A M.Enhanced moving K-means(EMKM)algorithm for image segmentation[J].IEEE transactions on consumer electronics,2015,57(2):833-841.
- [8]黄山,李众,李飞,等.基于改进粒子群和自适应滤波的快速模糊聚类图像分割[J].计算机测量与控制,2016,24(4):171-173.HUANG Shan,LI Zhong,LI Fei,et al.Image segmentation of fast fuzzy clustering based on improved particle swarm optimization and adaptive filtering[J].Computer measurement&control,2016,24(4):171-173.
- [9]DENG M H,LI Z C,ZHU S P.The agriculture vision image segmentation algorithm based on improved quantum-behaved particle swarm optimization[J].Applied mechanics&materials,2015,713-715:1947-1950.
- [10]GAO H,PUN C M,KWONG S.An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy[J].Information sciences,2016,369:500-521.
- [11]班俊硕,赖惠成,林宪峰,等.改进PSO与K均值聚类肤色分割的人脸检测算法[J].激光杂志,2017,38(2):82-86.BAN Junshuo,LAI Huicheng,LIN Xianfeng,et al.Human face detection algorithm for skin color segmentation using improved PSO and K-means clustering[J].Laser journal,2017,38(2):82-86.
- [12]GAO S,YANG J Y.Swarm intelligence algorithm and applications[M].Beijing:China Water&Power Press,2006.
- [13]刘桂红,赵亮,孙劲光,等.一种改进粒子群优化算法的Otsu图像阈值分割方法[J].计算机科学,2016,43(3):309-312.LIU Guihong,ZHAO Liang,SUN Jinguang,et al.Ostu image threshold segmentation method based on improved particle swarm optimization[J].Computer science,2016,43(3):309-312.
- [14]SHI Hao,LAI Huicheng,QIN Xizhong.Image segmentation algorithm of cotton based on PSO and K-means hybrid clustering[J].Computer engineering&applications,2013,49(21):226-229.
- [15]AVANIJA J,RAMAR K.A hybrid approach using PSO and Kmeans for semantic clustering of web documents[J].Journal of web engineering,2013,12(3/4):249-264.