Nonlinear relevance feedback: improving the performance of content-based retrieval systems

Doulamis, N; Doulamis, A; Kollias, S

dc.contributor.author	Doulamis, N	en
dc.contributor.author	Doulamis, A	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T02:41:39Z
dc.date.available	2014-03-01T02:41:39Z
dc.date.issued	2000	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30577
dc.subject	Content Based Retrieval	en
dc.subject	Relevance Feedback	en
dc.subject	Satisfiability	en
dc.subject	Neural Network	en
dc.title	Nonlinear relevance feedback: improving the performance of content-based retrieval systems	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICME.2000.869608	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICME.2000.869608	en
heal.publicationDate	2000	en
heal.abstract	In this paper, a non-linear relevance feedback mechanism is proposed for increasing the performance and the reliability of content-based retrieval systems. In particular, the human is considered as part of the retrieval process in an interactive framework, who evaluates the results provided by the system so that the system automatically updated its performance based on the users' feedback. An adaptively	en
heal.journalName	International Conference on Multimedia Computing and Systems/International Conference on Multimedia and Expo	en
dc.identifier.doi	10.1109/ICME.2000.869608	en