A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation

Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c -means (FCM) clustering has also been suc...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems Vol. 16; no. 5; pp. 1351 - 1361
Main Authors: Chatzis, S.P., Varvarigou, T.A.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clustering
Context modeling
Fuzzy
Fuzzy clustering
Fuzzy logic
Fuzzy set theory
Hidden Markov models
Image color analysis
Image edge detection
Image restoration
Image segmentation
Magnetorheological fluids
Markov processes
Markov random fields
Mathematical models
mean-field approximation
Parameter estimation
Pixel
Stochastic processes
Studies
ISSN:1063-6706, 1941-0034
Online Access:Get full text
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Summary:Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme, taking into account the mutual influences of neighboring sites, is asked for. Fuzzy c -means (FCM) clustering has also been successfully applied in several image segmentation applications. In this paper, we combine the benefits of these two approaches, by proposing a novel treatment of HMRF models, formulated on the basis of a fuzzy clustering principle. We approach the HMRF model treatment problem as an FCM-type clustering problem, effected by introducing the explicit assumptions of the HMRF model into the fuzzy clustering procedure. Our approach utilizes a fuzzy objective function regularized by Kullback--Leibler divergence information, and is facilitated by application of a mean-field-like approximation of the MRF prior. We experimentally demonstrate the superiority of the proposed approach over competing methodologies, considering a series of synthetic and real-world image segmentation applications.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2008.2005008