Knowledge-assisted image analysis based on context and spatial optimization

Papadopoulos, GTh; Mylonas, Ph; Mezaris, V; Avrithis, Y; Kompatsiaris, I

dc.contributor.author	Papadopoulos, GTh	en
dc.contributor.author	Mylonas, Ph	en
dc.contributor.author	Mezaris, V	en
dc.contributor.author	Avrithis, Y	en
dc.contributor.author	Kompatsiaris, I	en
dc.date.accessioned	2014-03-01T01:24:33Z
dc.date.available	2014-03-01T01:24:33Z
dc.date.issued	2006	en
dc.identifier.issn	1552-6283	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/17324
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33750086640&partnerID=40&md5=bf34453e72b7c1f226a01f306ac34527	en
dc.relation.uri	http://www.igi-pub.com/articles/details.asp?ID=6444	en
dc.relation.uri	http://www.informatik.uni-trier.de/~ley/db/journals/ijswis/ijswis2.html#PapadopoulosMMAK06	en
dc.subject	Context	en
dc.subject	Knowledge-assisted analysis	en
dc.subject	Multimedia ontologies	en
dc.subject	Semantic annotation	en
dc.subject	Semantic image analysis	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Information retrieval	en
dc.subject.other	Knowledge acquisition	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Optimization	en
dc.subject.other	Semantics	en
dc.subject.other	Knowledge assisted analysis	en
dc.subject.other	Multimedia ontology	en
dc.subject.other	Semantic annotations	en
dc.subject.other	Semantic image analysis	en
dc.subject.other	Image analysis	en
dc.title	Knowledge-assisted image analysis based on context and spatial optimization	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	In this article, an approach to semantic image analysis is presented. Under the proposed approach, ontologies are used to capture general, spatial, and contextual know ledge of a domain, and a genetic algorithm is applied to realize the final annotation. The employed domain knowledge considers high-level information in terms of the concepts of interest of the examined domain, contextual information in the form of fuzzy ontological relations, as well as low-level information in terms of prototypical low-level visual descriptors. To account for the inherent ambiguity in visual information, uncertainty has been introduced in the spatial relations definition. First, an initial hypothesis set of graded annotations is produced for each image region, and then context is exploited to update appropriately the estimated degrees of confidence. Finally, a genetic algorithm is applied to decide the most plausible annotation by utilizing the visual and the spatial concepts definitions included in the domain ontology. Experiments with a collection of photographs belonging to two different domains demonstrate the performance of the proposed approach. Copyright © 2006, Idea Group Inc.	en
heal.publisher	IGI PUBLISHING	en
heal.journalName	Semantic Web and Information Systems	en
dc.identifier.isi	ISI:000249760100003	en
dc.identifier.volume	2	en
dc.identifier.issue	3	en
dc.identifier.spage	17	en
dc.identifier.epage	36	en