Image segmentation via iterative fuzzy clustering based on local space-frequency multi-feature coherence criteria

Tzafestas, SG; Raptis, SN

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Raptis, SN	en
dc.date.accessioned	2014-03-01T01:15:39Z
dc.date.available	2014-03-01T01:15:39Z
dc.date.issued	2000	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/13642
dc.subject	fuzzy classification	en
dc.subject	fuzzy partition matrix	en
dc.subject	frequency connectivity	en
dc.subject	space connectivity	en
dc.subject	fuzzy segmentation	en
dc.subject	iterative fuzzy clustering (IFC)	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.subject.other	C-MEANS	en
dc.subject.other	ALGORITHMS	en
dc.subject.other	CLASSIFICATION	en
dc.title	Image segmentation via iterative fuzzy clustering based on local space-frequency multi-feature coherence criteria	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1008140930775	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1008140930775	en
heal.language	English	en
heal.publicationDate	2000	en
heal.abstract	Fuzzy set theory has recently attracted much attention in the field of image classification, image understanding and image processing. One of the major topics in fuzzy image processing is the image classification problem. This paper presents a fast and accurate iterative fuzzy clustering (I.F.C.) method dynamically adapted to the classification process. This is used for high performance fuzzy segmentation which forms the basis for reliable image understanding. The proposed fuzzy segmentation scheme examines the image connectivity in the space and frequency domains. The detected fuzzy features are combined via a block synthesis and local correlation algorithmic procedure. Some results showing that the performance of the proposed I.F.C./clustering method is superior from that of the standard fuzzy c-means method are provided.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS	en
dc.identifier.doi	10.1023/A:1008140930775	en
dc.identifier.isi	ISI:000087599400003	en
dc.identifier.volume	28	en
dc.identifier.issue	1-2	en
dc.identifier.spage	21	en
dc.identifier.epage	37	en