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dc.contributor.authorCheng, PCen_US
dc.contributor.authorChien, BCen_US
dc.contributor.authorKe, HRen_US
dc.contributor.authorYang, WPen_US
dc.date.accessioned2014-12-08T15:37:08Z-
dc.date.available2014-12-08T15:37:08Z-
dc.date.issued2005en_US
dc.identifier.isbn3-540-27420-0en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/25519-
dc.description.abstractThis paper aims at finding images that are similar to a medical image example query. We propose several image features based on wavelet coefficients, including color histogram, gray-spatial histogram, coherence moment, and gray correlogram, to facilitate the retrieval of similar medical images. The initial retrieval results are obtained via visual feature analysis. An automatic feedback mechanism that clusters visually and textually similar images among these initial results was also proposed to help refine the query. In the ImageCLEF 2004 evaluation, the experimental results show that our system is excellence in mean average precision.en_US
dc.language.isoen_USen_US
dc.titleSMIRE: Similar medical image retrieval engineen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.journalMULTILINGUAL INFORMATION ACCESS FOR TEXT, SPEECH AND IMAGESen_US
dc.citation.volume3491en_US
dc.citation.spage750en_US
dc.citation.epage760en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department圖書館zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentLibraryen_US
dc.identifier.wosnumberWOS:000231117600073-
Appears in Collections:Conferences Paper