Generalized nonlinear relevance feedback for interactive content-based retrieval and organization

Doulamis, AD; Doulamis, ND

dc.contributor.author	Doulamis, AD	en
dc.contributor.author	Doulamis, ND	en
dc.date.accessioned	2014-03-01T01:20:33Z
dc.date.available	2014-03-01T01:20:33Z
dc.date.issued	2004	en
dc.identifier.issn	1051-8215	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/15961
dc.subject	Image retrieval	en
dc.subject	MPEG-7	en
dc.subject	Relevance feedback	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Approximation theory	en
dc.subject.other	Data reduction	en
dc.subject.other	Database systems	en
dc.subject.other	Feedback	en
dc.subject.other	Iterative methods	en
dc.subject.other	Matrix algebra	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Sensitivity analysis	en
dc.subject.other	Average normalized modified retrieval rank (ANMRR)	en
dc.subject.other	Data ranking	en
dc.subject.other	MPEG-7	en
dc.subject.other	Relevance feedback	en
dc.subject.other	Content based retrieval	en
dc.title	Generalized nonlinear relevance feedback for interactive content-based retrieval and organization	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TCSVT.2004.826752	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TCSVT.2004.826752	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	In this paper, a novel relevance feedback algorithm is proposed for improving the performance of interactive content-based retrieval systems. The algorithm recursively estimates the similarity measure, which is used for data ranking in description environments where similarity-based queries are applied, using a set of relevant/irrelevant samples feedback by the user to the system so that the adjusted response is a better approximation of the current user's information needs and preferences. In particular, using concepts of functional analysis, the similarity measure is expressed as a parametric form of known monotone increasing functional components. Then, the contribution of each functional component to the similarity measure is estimated through a recursive and efficient on-line learning algorithm so that: 1) the current user's needs and preferences, as indicated by a set of selected relevant/irrelevant samples, are satisfied as much as possible, while simultaneously 2) a minimal modification of the already estimated similarity measure is accomplished. Experimental results on a large real-life database using objective evaluation criteria, such as the precision-recall curve and the average normalized modified retrieval rank (ANMRR), indicate that the proposed scheme outperforms the compared ones. In addition, the proposed algorithm requires low computational complexity and it can be implemented in a recursive way.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Circuits and Systems for Video Technology	en
dc.identifier.doi	10.1109/TCSVT.2004.826752	en
dc.identifier.isi	ISI:000221237700009	en
dc.identifier.volume	14	en
dc.identifier.issue	5	en
dc.identifier.spage	656	en
dc.identifier.epage	671	en