A relevance feedback approach for content based image retrieval using Gaussian Mixture models

Marakakis, A; Galatsanos, N; Likas, A; Stafylopatis, A

dc.contributor.author	Marakakis, A	en
dc.contributor.author	Galatsanos, N	en
dc.contributor.author	Likas, A	en
dc.contributor.author	Stafylopatis, A	en
dc.date.accessioned	2014-03-01T02:43:52Z
dc.date.available	2014-03-01T02:43:52Z
dc.date.issued	2006	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31531
dc.subject	Content Based Image Retrieval	en
dc.subject	Distance Metric	en
dc.subject	Gaussian Mixture	en
dc.subject	Gaussian Mixture Model	en
dc.subject	Probability Density Function	en
dc.subject	Relevance Feedback	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.other	Gaussian Mixture (GM) models	en
dc.subject.other	Relevance feedback (RF)	en
dc.subject.other	Feedback control	en
dc.subject.other	Information retrieval	en
dc.subject.other	Mathematical models	en
dc.subject.other	Neural networks	en
dc.subject.other	Probabilistic logics	en
dc.subject.other	Probability density function	en
dc.subject.other	Image processing	en
dc.title	A relevance feedback approach for content based image retrieval using Gaussian Mixture models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11840930_9	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11840930_9	en
heal.language	English	en
heal.publicationDate	2006	en
heal.abstract	In this paper a new relevance feedback (RF) methodology for content based image retrieval (CBIR) is presented. This methodology is based on Gaussian Mixture (GM) models for images. According to this methodology, the GM model of the query is updated in a probabilistic manner based on the GM models of the relevant images, whose relevance degree (positive or negative) is provided by the user. This methodology uses a recently proposed distance metric between probability density functions (pdfs) that can be computed in closed form for GM models. The proposed RF methodology takes advantage of the structure of this metric and proposes a method to update it very efficiently based on the GM models of the relevant and irrelevant images characterized by the user. We show with experiments the merits of the proposed methodology. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
heal.bookName	LECTURE NOTES IN COMPUTER SCIENCE	en
dc.identifier.doi	10.1007/11840930_9	en
dc.identifier.isi	ISI:000241475200009	en
dc.identifier.volume	4132 LNCS - II	en
dc.identifier.spage	84	en
dc.identifier.epage	93	en