Kernel generalized fuzzy c-means clustering with spatial information for image segmentation

The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray imag...

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Bibliographic Details
Published in:Digital signal processing Vol. 23; no. 1; pp. 184 - 199
Main Authors: Zhao, Feng, Jiao, Licheng, Liu, Hanqiang
Format: Journal Article
Language:English
Published: Elsevier Inc 01.01.2013
Subjects:
Algorithms
Clustering
Fuzzy
Fuzzy logic
Fuzzy set theory
Generalized fuzzy c-means clustering (GFCM)
Image segmentation
Kernel method
Kernels
Noise
Spatial information
Spatial information
Image segmentation
Generalized fuzzy c-means clustering (GFCM)
Kernel method
ISSN:1051-2004, 1095-4333
Online Access:Get full text
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Summary:The generalized fuzzy c-means clustering algorithm with improved fuzzy partition (GFCM) is a novel modified version of the fuzzy c-means clustering algorithm (FCM). GFCM under appropriate parameters can converge more rapidly than FCM. However, it is found that GFCM is sensitive to noise in gray images. In order to overcome GFCMʼs sensitivity to noise in the image, a kernel version of GFCM with spatial information is proposed. In this method, first a term about the spatial constraints derived from the image is introduced into the objective function of GFCM, and then the kernel induced distance is adopted to substitute the Euclidean distance in the new objective function. Experimental results show that the proposed method behaves well in segmentation performance and convergence speed for gray images corrupted by noise.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2012.09.016