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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Veröffentlicht in:	IEEE transactions on fuzzy systems Jg. 16; H. 5; S. 1351 - 1361
Hauptverfasser:	Chatzis, S.P., Varvarigou, T.A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.10.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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.
Author	Chatzis, S.P. Varvarigou, T.A.
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Snippet	Hidden Markov random field (HMRF) models have been widely used for image segmentation, as they appear naturally in problems where a spatially constrained...
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SubjectTerms	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
Title	A Fuzzy Clustering Approach Toward Hidden Markov Random Field Models for Enhanced Spatially Constrained Image Segmentation
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