Boosting Image Database Retrieval

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5927

Full metadata record

DC Field	Value	Language
dc.creator	Tieu, Kinh	-
dc.creator	Viola, Paul	-
dc.date	2004-10-04T14:15:20Z	-
dc.date	2004-10-04T14:15:20Z	-
dc.date	1999-09-10	-
dc.date.accessioned	2013-10-09T02:42:01Z	-
dc.date.available	2013-10-09T02:42:01Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1669	-
dc.identifier	http://hdl.handle.net/1721.1/5927	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	We present an approach for image database retrieval using a very large number of highly-selective features and simple on-line learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for generating a large number of complex features which capture some aspects of this causal structure. Boosting is used to learn simple and efficient classifiers in this complex feature space. Finally we will describe a practical implementation of our retrieval system on a database of 3000 images.	-
dc.format	7 p.	-
dc.format	10275632 bytes	-
dc.format	771855 bytes	-
dc.format	application/postscript	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1669	-
dc.subject	AI	-
dc.subject	MIT	-
dc.subject	Artificial Intelligence	-
dc.subject	Computer Vision	-
dc.subject	sImage Databases	-
dc.subject	Learning	-
dc.subject	Pattern Matching	-
dc.title	Boosting Image Database Retrieval	-
Appears in Collections:	MIT Items

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