Large scale concept detection in video using a region thesaurus

Spyrou, E; Tolias, G; Avrithis, Y

dc.contributor.author	Spyrou, E	en
dc.contributor.author	Tolias, G	en
dc.contributor.author	Avrithis, Y	en
dc.date.accessioned	2014-03-01T02:46:13Z
dc.date.available	2014-03-01T02:46:13Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32606
dc.subject	Cluster Algorithm	en
dc.subject	Feature Detection	en
dc.subject	Large Data Sets	en
dc.subject	Large Scale	en
dc.subject.other	Motion Picture Experts Group standards	en
dc.subject.other	Thesauri	en
dc.subject.other	Concept detections	en
dc.subject.other	Descriptors	en
dc.subject.other	High-level features	en
dc.subject.other	Key frames	en
dc.subject.other	Large data sets	en
dc.subject.other	Model vectors	en
dc.subject.other	Segmented regions	en
dc.subject.other	Trecvid	en
dc.subject.other	Video documents	en
dc.subject.other	Video shots	en
dc.subject.other	Clustering algorithms	en
dc.title	Large scale concept detection in video using a region thesaurus	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-92892-8_20	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-92892-8_20	en
heal.publicationDate	2009	en
heal.abstract	This paper presents an approach on high-level feature detection within video documents, using a Region Thesaurus. A video shot is represented by a single keyframe and MPEG-7 features are extracted locally, from coarse segmented regions. Then a clustering algorithm is applied on those extracted regions and a region thesaurus is constructed to facilitate the description of each keyframe at a higher level than the low-level descriptors but at a lower than the high-level concepts. A model vector representation is formed and several high-level concept detectors are appropriately trained using a global keyframe annotation. The proposed approach is thoroughly evaluated on the TRECVID 2007 development data for the detection of nine high level concepts, demonstrating sufficient performance on large data sets. © 2008 Springer Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-92892-8_20	en
dc.identifier.volume	5371 LNCS	en
dc.identifier.spage	197	en
dc.identifier.epage	207	en