Fuzzy support vector machines for image classification fusing MPEG-7 visual descriptors

Spyrou, E; Stamou, G; Avrithis, Y; Kollias, S

dc.contributor.author	Spyrou, E	en
dc.contributor.author	Stamou, G	en
dc.contributor.author	Avrithis, Y	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:50:05Z
dc.date.available	2014-03-01T02:50:05Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34893
dc.subject	Fuzzy Systems	en
dc.subject	Image Classification	en
dc.subject	MPEG-7 Descriptors	en
dc.subject	SVMs	en
dc.subject.other	Descriptors	en
dc.subject.other	Fuzzy basis functions	en
dc.subject.other	Fuzzy support vector machines	en
dc.subject.other	Fuzzy SVM	en
dc.subject.other	Generalization performance	en
dc.subject.other	High level semantics	en
dc.subject.other	Input space	en
dc.subject.other	Linguistic fuzzy rules	en
dc.subject.other	Low level descriptors	en
dc.subject.other	MPEG-7 descriptors	en
dc.subject.other	Numerical representation	en
dc.subject.other	Semantic gap	en
dc.subject.other	SVMs	en
dc.subject.other	Digital storage	en
dc.subject.other	Fuzzy logic	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Gears	en
dc.subject.other	Image analysis	en
dc.subject.other	Image classification	en
dc.subject.other	Knowledge representation	en
dc.subject.other	Motion Picture Experts Group standards	en
dc.subject.other	Semantics	en
dc.subject.other	Vector spaces	en
dc.subject.other	Support vector machines	en
dc.title	Fuzzy support vector machines for image classification fusing MPEG-7 visual descriptors	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1049/ic.2005.0706	en
heal.identifier.secondary	http://dx.doi.org/10.1049/ic.2005.0706	en
heal.publicationDate	2005	en
heal.abstract	This paper proposes a new type of a support vector machine which uses a kernel constituted from fuzzy basis functions. The proposed network combines the characteristics both of a support vector machine and a fuzzy system: high generalization performance, even when the dimension of the input space is very high, structured and numerical representation of knowledge and ability to extract linguistic fuzzy rules, in order to bridge the ""semantic gap"" between the low-level descriptors and the high-level semantics of an image. The Fuzzy SVM network was evaluated using images from the aceMedia Repository and more specifically in a beach/urban scenes classification problem.	en
heal.journalName	IET Seminar Digest	en
dc.identifier.doi	10.1049/ic.2005.0706	en
dc.identifier.volume	2005	en
dc.identifier.issue	11099	en
dc.identifier.spage	23	en
dc.identifier.epage	30	en