A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation

Chatzis, SP; Varvarigou, TA

dc.contributor.author	Chatzis, SP	en
dc.contributor.author	Varvarigou, TA	en
dc.date.accessioned	2014-03-01T01:27:40Z
dc.date.available	2014-03-01T01:27:40Z
dc.date.issued	2008	en
dc.identifier.issn	1063-6706	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18534
dc.subject	Fuzzy clustering	en
dc.subject	Hidden Markov models	en
dc.subject	Image segmentation	en
dc.subject	Mean-field approximation	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Applications	en
dc.subject.other	Digital image storage	en
dc.subject.other	Flow of solids	en
dc.subject.other	Fuzzy rules	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Fuzzy systems	en
dc.subject.other	Hidden Markov models	en
dc.subject.other	Image processing	en
dc.subject.other	Image segmentation	en
dc.subject.other	Markov processes	en
dc.subject.other	Polynomial approximation	en
dc.subject.other	Speech recognition	en
dc.subject.other	Clustering	en
dc.subject.other	Clustering approaches	en
dc.subject.other	Clustering problems	en
dc.subject.other	Clustering procedures	en
dc.subject.other	Constrained clustering	en
dc.subject.other	Fuzzy c-means	en
dc.subject.other	Fuzzy objective functions	en
dc.subject.other	Hidden Markov random field models	en
dc.subject.other	Hmrf models	en
dc.subject.other	Mean-field approximation	en
dc.subject.other	Fuzzy clustering	en
dc.title	A fuzzy clustering approach toward Hidden Markov random field models for enhanced spatially constrained image segmentation	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TFUZZ.2008.2005008	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TFUZZ.2008.2005008	en
heal.language	English	en
heal.publicationDate	2008	en
heal.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. © 2008 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Fuzzy Systems	en
dc.identifier.doi	10.1109/TFUZZ.2008.2005008	en
dc.identifier.isi	ISI:000260046700019	en
dc.identifier.volume	16	en
dc.identifier.issue	5	en
dc.identifier.spage	1351	en
dc.identifier.epage	1361	en