Modified Fast Fuzzy C-means Algorithm For Image Segmentation

摘要：

Because Fuzzy c-means (FCM) clustering algorithm has the problems of initializing the cluster centers and a huge number of computing in the iteration, this paper presents an improved method. It can optimize the data set to reduce the time for each of iteration, and then use cluster centers obtained by the sample density as the initial cluster centers to reduce the number of iterations required for convergence. Experiments show this method is able to solve the problem of initial centers, improve the speed of convergence and running and the clustering effects for image segmentation.

关键词： sample density fuzzy c-Means clustering data reduction initialization image segmentation

作者: Rong-chuan Guo Shui-sheng Ye Min Quan Hai-xia Shi

作者单位: College of Computer NanChang HangKong University Nanchang, China

会议类型: 国际会议

会议名称: Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)

会议地点: 南昌

会议语种:英文

页码: 695-699

在线出版日期: 2009-05-22（万方平台首次上网日期，不代表论文的发表时间）