Relevance feedback for content-based retrieval in video databases: a neural network approach

Doulamis, A; Doulamis, N; Kollias, S

dc.contributor.author	Doulamis, A	en
dc.contributor.author	Doulamis, N	en
dc.contributor.author	Kollias, S	en
dc.date.accessioned	2014-03-01T01:48:13Z
dc.date.available	2014-03-01T01:48:13Z
dc.date.issued	1999	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/25437
dc.subject	Content Based Retrieval	en
dc.subject	Cross Correlation	en
dc.subject	Genetic Algorithm	en
dc.subject	Motion Segmentation	en
dc.subject	Relevance Feedback	en
dc.subject	Video Database	en
dc.subject	Video Indexing	en
dc.subject	Feedforward Neural Network	en
dc.subject	Neural Network	en
dc.title	Relevance feedback for content-based retrieval in video databases: a neural network approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/ICECS.1999.814514	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICECS.1999.814514	en
heal.publicationDate	1999	en
heal.abstract	A neural network scheme is presented for adaptive video indexing and retrieval. First, a limited but characteristic amount of frames are extracted from each video scene, able to provide an efficient representation of the video content. For this reason, a cross correlation criterion is mini-cited using a genetic algorithm. Low level features are extracted to indicate the frame characteristics, such	en
dc.identifier.doi	10.1109/ICECS.1999.814514	en