On the selection of MPEG-7 visual descriptors and their level of detail for nature disaster video sequences classification

Molina, J; Spyrou, E; Sofou, N; Martinez, JM

dc.contributor.author	Molina, J	en
dc.contributor.author	Spyrou, E	en
dc.contributor.author	Sofou, N	en
dc.contributor.author	Martinez, JM	en
dc.date.accessioned	2014-03-01T02:44:52Z
dc.date.available	2014-03-01T02:44:52Z
dc.date.issued	2007	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31991
dc.subject	Image classification	en
dc.subject	Semantic retrieval	en
dc.subject	Visual descriptors	en
dc.subject.other	Image classification	en
dc.subject.other	Image retrieval	en
dc.subject.other	Semantic Web	en
dc.subject.other	Support vector machines	en
dc.subject.other	Semantic retrieval	en
dc.subject.other	Visual descriptors	en
dc.subject.other	Motion Picture Experts Group standards	en
dc.title	On the selection of MPEG-7 visual descriptors and their level of detail for nature disaster video sequences classification	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-540-77051-0_6	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-540-77051-0_6	en
heal.publicationDate	2007	en
heal.abstract	In this paper, we present a study on the discrimination capabilities of colour, texture and shape MPEG-7 [ 1 ] visual descriptors, within the context of video sequences. The target is to facilitate the recognition of certain visual cues which would then allow the classification of natural disaster-related concepts. Low-level visual features are extracted using the MPEG-7 ""eXperimentation Module"" (XM) [2]. The extraction times associated to the levels of detail of the descriptors are measured. The pattern sets obtained as combination of significant levels of detail of different descriptors are the input to a Support Vector Machine (SVM), resulting on the classification accuracies. Preliminary results indicate that this approach could be useful for the implementation of real-time spatial regions classifiers. © Springer-Verlag Berlin Heidelberg 2007.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-540-77051-0_6	en
dc.identifier.volume	4816 LNCS	en
dc.identifier.spage	70	en
dc.identifier.epage	73	en