Boosting Image Database Retrieval

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

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