Relevance feedback for content-based image retrieval using support vector machines and feature selection

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	2014-03-01T02:46:30Z
dc.date.available	2014-03-01T02:46:30Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32677
dc.subject	Content-based image retrieval	en
dc.subject	Feature selection	en
dc.subject	Relevance feedback	en
dc.subject	Support vector machines	en
dc.subject.other	Classification tasks	en
dc.subject.other	Content-based image retrieval	en
dc.subject.other	Database images	en
dc.subject.other	Feature selection	en
dc.subject.other	Image features	en
dc.subject.other	Multidimensional vectors	en
dc.subject.other	Negative examples	en
dc.subject.other	Numerical experiments	en
dc.subject.other	Relevance feedback	en
dc.subject.other	Shape information	en
dc.subject.other	SVM classifiers	en
dc.subject.other	Backpropagation	en
dc.subject.other	Classifiers	en
dc.subject.other	Content based retrieval	en
dc.subject.other	Feedback	en
dc.subject.other	Information retrieval	en
dc.subject.other	Neural networks	en
dc.subject.other	Support vector machines	en
dc.subject.other	Vectors	en
dc.subject.other	Feature extraction	en
dc.title	Relevance feedback for content-based image retrieval using support vector machines and feature selection	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-04274-4_97	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-04274-4_97	en
heal.publicationDate	2009	en
heal.abstract	A relevance feedback (RF) approach for content-based image retrieval (CBIR) is proposed, which is based on Support Vector Machines (SVMs) and uses a feature selection technique to reduce the dimensionality of the image feature space. Specifically, each image is described by a multidimensional vector combining color, texture and shape information. In each RF round, the positive and negative examples provided by the user are used to determine a relatively small number of the most important features for the corresponding classification task, via a feature selection methodology. After the feature selection has been performed, an SVM classifier is trained to distinguish between relevant and irrelevant images according to the preferences of the user, using the restriction of the user examples on the set of selected features. The trained classifier is subsequently used to provide an updated ranking of the database images represented in the space of the selected features. Numerical experiments are presented that demonstrate the merits of the proposed relevance feedback methodology. © 2009 Springer Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-04274-4_97	en
dc.identifier.volume	5768 LNCS	en
dc.identifier.issue	PART 1	en
dc.identifier.spage	942	en
dc.identifier.epage	951	en