Probabilistic relevance feedback approach for content-based image retrieval based on 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	2014-03-01T01:31:42Z
dc.date.available	2014-03-01T01:31:42Z
dc.date.issued	2009	en
dc.identifier.issn	1751-9659	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19897
dc.subject	Content Based Image Retrieval	en
dc.subject	Gaussian Mixture Model	en
dc.subject	Relevance Feedback	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Content based retrieval	en
dc.subject.other	Feature extraction	en
dc.subject.other	Feedback	en
dc.subject.other	Granular materials	en
dc.subject.other	Image retrieval	en
dc.subject.other	Information retrieval	en
dc.subject.other	Probability density function	en
dc.subject.other	Trellis codes	en
dc.subject.other	Closed forms	en
dc.subject.other	Content-Based Image retrievals	en
dc.subject.other	Distance measures	en
dc.subject.other	Gaussian mixture models	en
dc.subject.other	Gaussian mixtures	en
dc.subject.other	GM models	en
dc.subject.other	Image features	en
dc.subject.other	Image models	en
dc.subject.other	Models of the users	en
dc.subject.other	Negative feedbacks	en
dc.subject.other	Numerical experiments	en
dc.subject.other	Probability densities	en
dc.subject.other	Relevance feedbacks	en
dc.subject.other	Control theory	en
dc.title	Probabilistic relevance feedback approach for content-based image retrieval based on gaussian mixture models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1049/iet-ipr:20080012	en
heal.identifier.secondary	http://dx.doi.org/10.1049/iet-ipr:20080012	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	A new relevance feedback (RF) approach for content-based image retrieval is presented. This approach uses Gaussian mixture (GM) models of the image features and a query that is updated in a probabilistic manner. This update reflects the preferences of the user and is based on the models of both the positive and negative feedback images. The retrieval is based on a recently proposed distance measure between probability density functions, which can be computed in closed form for GM models. The proposed approach takes advantage of the form of this distance measure and updates it very efficiently based on the models of the user-specified relevant and irrelevant images. It is also shown that this RF framework is fairly general and can be applied in case other image models or distance measures are used instead of those proposed in this work. Finally, comparative numerical experiments are provided, which that demonstrate the merits of the proposed RF methodology and the use of the distance measure, and also the advantages of using GMs for image modelling. © 2009 The Institution of Engineering and Technology.	en
heal.publisher	INST ENGINEERING TECHNOLOGY-IET	en
heal.journalName	IET Image Processing	en
dc.identifier.doi	10.1049/iet-ipr:20080012	en
dc.identifier.isi	ISI:000263904800002	en
dc.identifier.volume	3	en
dc.identifier.issue	1	en
dc.identifier.spage	10	en
dc.identifier.epage	25	en