Optimal recursive similarity measure estimation for interactive content-based image retrieval

Doulamis, N; Doulamis, A

dc.contributor.author	Doulamis, N	en
dc.contributor.author	Doulamis, A	en
dc.date.accessioned	2014-03-01T02:42:08Z
dc.date.available	2014-03-01T02:42:08Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30810
dc.subject	Content Based Image Retrieval	en
dc.subject	Feature Vector	en
dc.subject	Functional Analysis	en
dc.subject	Recursive Algorithm	en
dc.subject	Relevance Feedback	en
dc.subject	Similarity Measure	en
dc.subject	Taylor Series Expansion	en
dc.subject	First Order	en
dc.subject.other	Algorithms	en
dc.subject.other	Feedback	en
dc.subject.other	Interactive computer graphics	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Functional analysis	en
dc.subject.other	Recursive algorithm	en
dc.subject.other	Similarity estimation	en
dc.subject.other	Content based retrieval	en
dc.title	Optimal recursive similarity measure estimation for interactive content-based image retrieval	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2002.1038190	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2002.1038190	en
heal.publicationDate	2002	en
heal.abstract	In this paper, a new recursive algorithm is proposed for optimal estimation of similarity measure used in a content-based retrieval system. This is performed through a relevance feedback mechanism, which adjusts the similarity distance using information fed back to the user according to the relevance of the previously retrieved images. On contrast to conventional relevance feedback schemes to which a degree of importance is assigned to each element of the feature vector describing the image content, the proposed algorithm optimally adapts the similarity measure at each feedback iteration. This is performed by modeling the similarity distance using functional analysis. The algorithm assumes that a small modification of the similarity measure parameters is adequate to adapt the system response to the new user's requirements. In this case, a first-order Taylor series expansion can be applied and a computationally efficient scheme can be implemented to estimate the optimal similarity measure.	en
heal.journalName	IEEE International Conference on Image Processing	en
dc.identifier.doi	10.1109/ICIP.2002.1038190	en
dc.identifier.volume	1	en
dc.identifier.spage	I/972	en
dc.identifier.epage	I/975	en