现代电子技术2018，Vol.41Issue(7)：36-40,5.DOI:10.16652/j.issn.1004-373x.2018.07.010
基于模拟退火和粒子群改进的图像分割FCM方法
Improved FCM method for image segmentation based on simulated annealing and particle swarm optimization
摘要
Abstract
Since the traditional fuzzy C-means(FCM)algorithm needs to give the clustering numbers in advance and is easy to fall into local minimum for image segmentation,a novel FCM algorithm based on simulated annealing algorithm and parti-cle swarm optimization(PSO)is proposed. The PSO algorithm is applied to update the clustering center of FCM to enhance the search ability and convergence rate of the algorithm. And then the simulated annealing rule is used to decide whether to accept the new clustering center or not,so as to obtain the global optimal value of current iteration. The validity function is set to find the optimal clustering numbers of the image,and make the proposed algorithm have the ability to adaptively judge the numbers of an image category. The experiment results demonstrate that the algorithm has perfect global convergence,and is able to adap-tively find the optimum catelory numbers of the image in the case of the unknown clustering numbers.关键词
自适应图像分割/模拟退火算法/粒子群算法/模糊C均值/聚类中心/全局最优Key words
adaptive image segmentation/simulated annealing algorithm/particle swarm optimization/fuzzy C-means/clustering center/global optimization分类
信息技术与安全科学引用本文复制引用
陆振宇,夏志巍,卢亚敏,黄现云..基于模拟退火和粒子群改进的图像分割FCM方法[J].现代电子技术,2018,41(7):36-40,5.基金项目
国家自然科学基金面上项目(61473334) Project Supported by National Natural Science Foundation of China(61473334) （61473334）
