Intelligent content retrieval using a visual vocabulary and geometric constraints

Spyrou, E; Kalantidis, Y; Tolias, G; Mylonas, P; Kollias, S

dc.contributor.author	Spyrou, E	en
dc.contributor.author	Kalantidis, Y	en
dc.contributor.author	Tolias, G	en
dc.contributor.author	Mylonas, P	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:46:51Z
dc.date.available	2014-03-01T02:46:51Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32889
dc.subject	Digital Content	en
dc.subject	Geometric Constraints	en
dc.subject	Image Analysis	en
dc.subject	Local Features	en
dc.subject	Multimedia Data	en
dc.subject	High Definition	en
dc.subject.other	Content retrieval	en
dc.subject.other	Content-based	en
dc.subject.other	Digital contents	en
dc.subject.other	Digital still images	en
dc.subject.other	Geometric constraint	en
dc.subject.other	Geometrical constraints	en
dc.subject.other	High definition	en
dc.subject.other	Image datasets	en
dc.subject.other	Intelligent extensions	en
dc.subject.other	Local feature extraction	en
dc.subject.other	Multimedia contents	en
dc.subject.other	Multimedia data	en
dc.subject.other	Multimedia processing	en
dc.subject.other	Personal Content	en
dc.subject.other	Visual vocabularies	en
dc.subject.other	Feature extraction	en
dc.subject.other	Artificial intelligence	en
dc.title	Intelligent content retrieval using a visual vocabulary and geometric constraints	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/FUZZY.2010.5584000	en
heal.identifier.secondary	http://dx.doi.org/10.1109/FUZZY.2010.5584000	en
heal.identifier.secondary	5584000	en
heal.publicationDate	2010	en
heal.abstract	During the last decades multimedia processing has emerged as an important technology to retrieve content based on similar data. Moreover, recent developments in the fields of high definition (HD) multimedia content and personal content collections (personal camcorders and digital still image cameras) tend to generate a huge volume of multimedia data everyday. Thus, the need for a meaningful, quick organization and access to generated content is now more than necessary; however, it still remains a rather difficult problem to be tackled both by humans and computers. In this paper we propose an intelligent extension of traditional image analysis methodologies towards more efficient digital content retrieval. The main idea is to extend local feature extraction methodologies by introducing additional geometrical constraints in the process. The proposed approach is tested and evaluated on a number of publicly available image datasets and results are very promising. © 2010 IEEE.	en
heal.journalName	2010 IEEE World Congress on Computational Intelligence, WCCI 2010	en
dc.identifier.doi	10.1109/FUZZY.2010.5584000	en