Adaptive multimedia content personalization

Doulamis, ND; Georgilakis, P

dc.contributor.author	Doulamis, ND	en
dc.contributor.author	Georgilakis, P	en
dc.date.accessioned	2014-03-01T02:42:26Z
dc.date.available	2014-03-01T02:42:26Z
dc.date.issued	2004	en
dc.identifier.issn	02714310	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31006
dc.subject	Cross Correlation	en
dc.subject	Functional Analysis	en
dc.subject	Human Perception	en
dc.subject	Interactive Multimedia	en
dc.subject	linear functionals	en
dc.subject	Multimedia Data	en
dc.subject	Relevance Feedback	en
dc.subject	Similarity Measure	en
dc.subject	User Preferences	en
dc.subject.other	Average normalized modified retrieval rank (ANMRR)	en
dc.subject.other	Content-based retrieval (CBIR) systems	en
dc.subject.other	Learning techniques	en
dc.subject.other	Motion picture expert group (MPEG)	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer simulation	en
dc.subject.other	Content based retrieval	en
dc.subject.other	Correlation methods	en
dc.subject.other	Feedback	en
dc.subject.other	Image analysis	en
dc.subject.other	Iterative methods	en
dc.subject.other	Lagrange multipliers	en
dc.subject.other	Motion pictures	en
dc.subject.other	Perturbation techniques	en
dc.subject.other	Radio broadcasting	en
dc.subject.other	Television broadcasting	en
dc.subject.other	Vectors	en
dc.subject.other	Multimedia systems	en
dc.title	Adaptive multimedia content personalization	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ISCAS.2004.1329240	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ISCAS.2004.1329240	en
heal.publicationDate	2004	en
heal.abstract	Modeling multimedia content by identifying semantically meaningful entities can be arduous because it is difficult to simulate human perception. However, by creating an algorithm to respond interactively to user preference, content-retrieval systems can become more efficient and easier to use. In this paper, we investigate adaptive relevance feedback algorithms for interactive multimedia content personalization. In particular two interesting scenarios are examined. The first uses a weighted cross correlation similarity measure for ranking multimedia data. The second exploits concepts of functional analysis to model the similarity measure as a non-linear function, the type of which is estimated by the users' preferences. The algorithms are computationally efficient and they can be recursively implemented.	en
heal.journalName	Proceedings - IEEE International Symposium on Circuits and Systems	en
dc.identifier.doi	10.1109/ISCAS.2004.1329240	en
dc.identifier.volume	2	en
dc.identifier.spage	II189	en
dc.identifier.epage	II192	en