Application of relevance feedback in 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	2014-03-01T02:45:07Z
dc.date.available	2014-03-01T02:45:07Z
dc.date.issued	2008	en
dc.identifier.issn	10823409	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32163
dc.subject	Content Based Image Retrieval	en
dc.subject	Distance Measure	en
dc.subject	Gaussian Mixture	en
dc.subject	Gaussian Mixture Model	en
dc.subject	Image Features	en
dc.subject	Image Retrieval	en
dc.subject	Negative Feedback	en
dc.subject	Probability Density Function	en
dc.subject	Relevance Feedback	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Content based retrieval	en
dc.subject.other	Control theory	en
dc.subject.other	Feedback	en
dc.subject.other	Granular materials	en
dc.subject.other	Image enhancement	en
dc.subject.other	Image retrieval	en
dc.subject.other	Information retrieval	en
dc.subject.other	Photographic accessories	en
dc.subject.other	Probability	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	en
dc.subject.other	Gaussian Mixture models	en
dc.subject.other	GM models	en
dc.subject.other	Image features	en
dc.subject.other	Models of the users	en
dc.subject.other	Negative feedbacks	en
dc.subject.other	Probability densities	en
dc.subject.other	Relevance feedbacks	en
dc.subject.other	Probability density function	en
dc.title	Application of relevance feedback in content based image retrieval using gaussian mixture models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICTAI.2008.110	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICTAI.2008.110	en
heal.identifier.secondary	4669682	en
heal.publicationDate	2008	en
heal.abstract	In this paper a relevance feedback (RF) approach for content based image retrieval (CBIR) is described and evaluated. The 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 positive and negative feedback images. Retrieval is based on a recently proposed distance measure between probability density functions (pdfs), 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. For evaluation purposes, comparative experimental results are presented that demonstrate the merits of the proposed methodology. © 2008 IEEE.	en
heal.journalName	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI	en
dc.identifier.doi	10.1109/ICTAI.2008.110	en
dc.identifier.volume	1	en
dc.identifier.spage	141	en
dc.identifier.epage	148	en