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

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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


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