Fusing MPEG-7 visual descriptors for image classification

Spyrou, E; Borgne, HL; Mailis, T; Cooke, E; Avrithis, Y; O'Connor, N

dc.contributor.author	Spyrou, E	en
dc.contributor.author	Borgne, HL	en
dc.contributor.author	Mailis, T	en
dc.contributor.author	Cooke, E	en
dc.contributor.author	Avrithis, Y	en
dc.contributor.author	O'Connor, N	en
dc.date.accessioned	2014-03-01T02:43:20Z
dc.date.available	2014-03-01T02:43:20Z
dc.date.issued	2005	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31344
dc.subject	Back Propagation	en
dc.subject	Euclidean Distance	en
dc.subject	Fuzzy Rules	en
dc.subject	Image Classification	en
dc.subject	K Nearest Neighbor	en
dc.subject	Machine Learning	en
dc.subject	Semantic Gap	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Backpropagation	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Imaging techniques	en
dc.subject.other	Neural networks	en
dc.subject.other	Problem solving	en
dc.subject.other	Semantics	en
dc.subject.other	Euclidean distance	en
dc.subject.other	Fuzzy-ART neurofuzzy network	en
dc.subject.other	Image classification	en
dc.subject.other	MPEG-7 visual descriptors	en
dc.subject.other	Image analysis	en
dc.title	Fusing MPEG-7 visual descriptors for image classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11550907_134	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11550907_134	en
heal.language	English	en
heal.publicationDate	2005	en
heal.abstract	This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A ""merging"" fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the ""semantic gap"" between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem. © Springer-Verlag Berlin Heidelberg 2005.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11550907_134	en
dc.identifier.isi	ISI:000232196000134	en
dc.identifier.volume	3697 LNCS	en
dc.identifier.spage	847	en
dc.identifier.epage	852	en