Multiple Resolution Image Classification

dc.creator	Bouvrie, Jake V.
dc.date	2004-10-08T20:38:35Z
dc.date	2004-10-08T20:38:35Z
dc.date	2002-12-01
dc.date.accessioned	2013-10-09T02:46:30Z
dc.date.available	2013-10-09T02:46:30Z
dc.date.issued	2013-10-09
dc.identifier	AIM-2002-022
dc.identifier	http://hdl.handle.net/1721.1/6705
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	Binary image classifiction is a problem that has received much attention in recent years. In this paper we evaluate a selection of popular techniques in an effort to find a feature set/ classifier combination which generalizes well to full resolution image data. We then apply that system to images at one-half through one-sixteenth resolution, and consider the corresponding error rates. In addition, we further observe generalization performance as it depends on the number of training images, and lastly, compare the system's best error rates to that of a human performing an identical classification task given teh same set of test images.
dc.format	1054982 bytes
dc.format	824527 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-2002-022
dc.subject	AI
dc.title	Multiple Resolution Image Classification

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