Using visual context and region semantics for high-level concept detection

Mylonas, P; Spyrou, E; Avrithis, Y; Kollias, S

dc.contributor.author	Mylonas, P	en
dc.contributor.author	Spyrou, E	en
dc.contributor.author	Avrithis, Y	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:32:23Z
dc.date.available	2014-03-01T01:32:23Z
dc.date.issued	2009	en
dc.identifier.issn	1520-9210	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20103
dc.subject	Concept detection	en
dc.subject	Contextualization	en
dc.subject	Region thesaurus	en
dc.subject	Region types	en
dc.subject	Visual context	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Computer Science, Software Engineering	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Information theory	en
dc.subject.other	Semantics	en
dc.subject.other	Concept detection	en
dc.subject.other	Contextualization	en
dc.subject.other	Region thesaurus	en
dc.subject.other	Region types	en
dc.subject.other	Visual context	en
dc.subject.other	Thesauri	en
dc.title	Using visual context and region semantics for high-level concept detection	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TMM.2008.2009681	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TMM.2008.2009681	en
heal.identifier.secondary	4757439	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	In this paper we investigate detection of high-level concepts in multimedia content through an integrated approach of visual thesaurus analysis and visual context. In the former, detection is based on model vectors that represent image composition in terms of region types, obtained through clustering over a large data set. The latter deals with two aspects, namely high-level concepts and region types of the thesaurus, employing a model of a priori specified semantic relations among concepts and automatically extracted topological relations among region types; thus it combines both conceptual and topological context. A set of algorithms is presented, which modify either the confidence values of detected concepts, or the model vectors based on which detection is performed. Visual context exploitation is evaluated on TRECVID and Corel data sets and compared to a number of related visual thesaurus approaches. © 2009 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Multimedia	en
dc.identifier.doi	10.1109/TMM.2008.2009681	en
dc.identifier.isi	ISI:000262714800005	en
dc.identifier.volume	11	en
dc.identifier.issue	2	en
dc.identifier.spage	229	en
dc.identifier.epage	243	en