Title :
Modified Fast Fuzzy C-means Algorithm for Image Segmentation
Author :
Guo, Rong-Chuan ; Ye, Shui-sheng ; Quan, Min ; Shi, Hai-Xia
Author_Institution :
Coll. of Comput., NanChang HangKong Univ., Nanchang, China
Abstract :
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.
Keywords :
fuzzy set theory; image segmentation; pattern clustering; clustering algorithm; fast fuzzy c-means algorithm; image segmentation; Clustering algorithms; Clustering methods; Computer security; Convergence; Educational institutions; Electronic commerce; Image segmentation; Iterative algorithms; Mathematics; Partitioning algorithms; data reduction; fuzzy c-Means clustering; image segmentation; initialization; sample density;
Conference_Titel :
Electronic Commerce and Security, 2009. ISECS '09. Second International Symposium on
Conference_Location :
Nanchang
Print_ISBN :
978-0-7695-3643-9
DOI :
10.1109/ISECS.2009.22
