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

作　　者：陆振宇[1,2] 夏志巍 卢亚敏 黄现云 LU Zhenyu;XIA Zhiwe;LU Yamin;HUANG Xianyun(School of Electronic & Information Engineering,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment,Nanjing 210044,China)

机构地区：[1]南京信息工程大学电子与信息学院,江苏南京210044 [2]江苏省大气环境与装备技术协同创新中心,江苏南京210044

出　　处：《现代电子技术》2018年第7期36-40,共5页Modern Electronics Technique

基　　金：国家自然科学基金面上项目(61473334)~~

摘　　要：针对模糊C均值聚类(FCM)算法在分割图像时需要事先给出聚类数和容易陷入局部极小值的问题,提出一种新的FCM算法。首先,利用粒子群算法更新FCM的聚类中心,以加强算法的搜索能力,提高收敛速度;其次,根据模拟退火准则决定是否接受新的聚类中心,以得到当前迭代下的全局最优值;最后,设定有效性函数寻找图像的最佳聚类数,使算法具有自适应判断图像类别个数的能力。实验结果表明,该算法具有较好的全局收敛性,并且在未知聚类数的情况下能自适应寻找图像的最佳分类个数。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 

分 类 号：TN911.73-34[电子电信—通信与信息系统]