Intelligent content retrieval using a visual vocabulary and geometric constraints

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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 http://hdl.handle.net/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 5584000 en
heal.identifier.secondary http://dx.doi.org/10.1109/FUZZY.2010.5584000 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

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