A generic fuzzy rule based image segmentation algorithm

被引:58
作者
Karmakar, GC [1 ]
Dooley, LS [1 ]
机构
[1] Monash Univ, Gippsland Sch Comp & Informat Technol, Churchill, Vic 3842, Australia
关键词
generic fuzzy rules; image segmentation; spatial information; fuzzy clustering;
D O I
10.1016/S0167-8655(02)00069-7
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Fuzzy rule based image segmentation techniques tend in general, to be application dependent with the structure of the membership functions being predefined and in certain cases, the corresponding parameters being manually determined. The net result is that the overall performance of the segmentation technique is very sensitive to parameter value selections. This paper addresses these issues by introducing a generic fuzzy rule based image segmentation (GFRIS) algorithm, which is both application independent and exploits inter-pixel spatial relationships. The GFRIS algorithm automatically approximates both the key weighting factor and threshold value in the definitions of the fuzzy rule and neighbourhood system, respectively. A quantitative evaluation is presented between the segmentation results obtained using GFRIS and the popular fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. The results demonstrate that GFRIS exhibits a considerable improvement in performance compared to both FCM and PCM, for many different image types. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:1215 / 1227
页数:13
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