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

Volume

2

fYear

2009

fDate

22-24 May 2009

Firstpage

39

Lastpage

43

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Commerce and Security, 2009. ISECS '09. Second International Symposium on

Conference_Location

Nanchang

Print_ISBN

978-0-7695-3643-9

Type

conf

DOI

10.1109/ISECS.2009.22

Filename

5209827