Image Segmentation Based on Adaptive Cluster Prototype Estimation

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Title Image Segmentation Based on Adaptive Cluster Prototype Estimation
Author Liew, Alan Wee-Chung; Yan, Hong; Law, N.F.
Journal Name IEEE Transactions on Fuzzy Systems
Year Published 2005
Place of publication United States
Publisher Institute of Electrical and Electronics Engineers
Abstract An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper. In the conventional FCM clustering algorithm, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and does not take into consideration the spatial distribution of pixels in an image. By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial continuity, thus exploiting the high inter-pixel correlation inherent in most real-world images. The incorporation of local spatial continuity allows the suppression of noise and helps to resolve classification ambiguity. To account for smooth intensity variation within each homogenous region in an image, a multiplicative field is introduced to each of the fixed FCM cluster prototype. The multiplicative field effectively makes the fixed cluster prototype adaptive to slow smooth within-cluster intensity variation, and allows homogenous regions with slow smooth intensity variation to be segmented as a whole. Experimental results with synthetic and real color images have shown the effectiveness of the proposed algorithm.
Peer Reviewed Yes
Published Yes
Publisher URI http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=91
Alternative URI http://dx.doi.org/10.1109/TFUZZ.2004.841748
Copyright Statement Copyright 2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Volume 13
Issue Number 4
Page from 444
Page to 453
ISSN 1063-6706
Date Accessioned 2007-06-08
Date Available 2009-03-26T06:42:29Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject PRE2009-Image Processing
URI http://hdl.handle.net/10072/21801
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1x

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