DocumentCode :
2765366
Title :
An Image Segmentation Algorithm Based on Fuzzy C-Means Clustering
Author :
Zhang, Xin-Bo ; Jiang, Li
Author_Institution :
Coll. of Inf. & Electron. Eng., ZheJiang Gongs hang Univ., Hangzhou, China
fYear :
2009
fDate :
7-9 March 2009
Firstpage :
22
Lastpage :
26
Abstract :
Image segmentation algorithm based on fuzzy c-means clustering is an important algorithm in the image segmentation field. It has been used widely. However, it is not successfully to segment the noise image because the algorithm disregards of special constraint information. It only considers the gray information. Therefore, we proposed a weighed FCM algorithm based on Gaussian kernel function for image segmentation. The original Euclidean distance is replaced by a kernel-induced distance in the algorithm. Then, a bound term is added to the objective function to compensate the influence of the spatial information. The experimental results illustrate that the proposed method is more effective to image segmentation.
Keywords :
fuzzy set theory; image segmentation; pattern clustering; Euclidean distance; Gaussian kernel function; fuzzy c-means clustering; gray information; image segmentation algorithm; kernel-induced distance; noise image; objective function; weighed FCM algorithm; Clustering algorithms; Data analysis; Density functional theory; Digital images; Educational institutions; Euclidean distance; Image segmentation; Kernel; Parameter estimation; Prototypes; fuzzy c-means; gaussian kernel function; image segmentation; weighted;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Image Processing, 2009 International Conference on
Conference_Location :
Bangkok
Print_ISBN :
978-0-7695-3565-4
Type :
conf
DOI :
10.1109/ICDIP.2009.15
Filename :
5190584
Link To Document :
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