融合混沌优化和改进模糊聚类的图像分割算法  

A Novel Algorithm by Incorporating Chaos Optimization and Improved Fuzzy C-Means for Image Segmentation

作　　者：朱占龙 刘永军 ZHU Zhan-long;LIU Yong-jun(School of Information Engineering,Heibei GEO University,Shijiazhuang,Hebei 050031,China;Hebei Key Laboratory of Optoelectronic Information and Geo-Detection Technology,Shijiazhuang,Hebei 050031,China;Laboratory of Artificial Intelligence and Machine Learning,Heibei GEO University,Shijiazhuang,Hebei 050031,China)

机构地区：[1]河北地质大学信息工程学院,河北石家庄050031 [2]河北省光电信息与地球探测技术重点实验室,河北石家庄050031 [3]河北地质大学人工智能与机器学习研究室,河北石家庄050031

出　　处：《电子学报》2020年第5期975-984,共10页Acta Electronica Sinica

基　　金：河北省高等学校科学技术研究(No.BJ2018029);河北省教育厅重点(No.ZD2018212);河北地质大学博士科研启动基金(No.BQ201606)。

摘　　要：基于邻域广义模糊聚类算法能够分割含噪声灰度图像,但是如果图像灰度分布不均衡或者起始的聚类中心设置不合适仍会导致该算法分割失败,为此,提出一种基于混沌优化和改进模糊聚类算法相融合的图像分割算法.首先,将每一类的隶属度之和引入基于邻域广义模糊聚类算法的目标函数中,从而能够均衡较大类和较小类对目标函数的贡献.其次,以新目标函数为基础,利用拉格朗日乘子法推导出相应的隶属度和聚类中心.再次,将混沌优化和改进模糊聚类算法联合得到最优解,即最合适的聚类中心,细节上,每一代的聚类中心分别由混沌系统和改进模糊聚类算法两种路径产生,具有较小目标函数的聚类中心进入下一个迭代进程.最后,利用具有不平衡特性的无损检测图像进行实验,结果表明本文算法具有更高的分割准确率和更好的视觉效果.The spatial generalized fuzzy c-means clustering algorithm(GFCM_S)is a popular technique for image segmentation,but it is not so effective when the image has the features of unequal cluster sizes or the initial cluster centers we choose are improper.In this paper,for solving the above shortcomings of GFCM_S,a novel algorithm incorporating chaos optimization and improved fuzzy c-means(CIGFCM_S)is proposed.Firstly,each size of clusters is integrated into the objective function of GFCM_S so as to equalize the contribution of larger and smaller clusters to the objective function.Secondly,the iteratively membership degree and cluster centers are deduced by the Lagrange multiplier method.Thirdly,a new iterative strategy is used to seek the optimal solutions.In detail,the optimal solutions of next generation are searched by two-paths,one path originates chaos optimization and the other is obtained by updating membership degree and cluster centers on the basis of current optimal solutions,and then the better solutions go to next generation until the end.Lastly,the non-destructive testing(NDT)images with the characters of unequal cluster sizes are used for experiments,the results show that the proposed algorithm has better segmentation accuracy and visual effects.

关 键 词：图像分割 混沌优化 模糊聚类 灰度分布不均衡 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]