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dc.contributor.authorPao, H. T.en_US
dc.contributor.authorXu, Y. Y.en_US
dc.contributor.authorChuang, S. C.en_US
dc.contributor.authorFu, H. C.en_US
dc.date.accessioned2014-12-08T15:07:00Z-
dc.date.available2014-12-08T15:07:00Z-
dc.date.issued2007en_US
dc.identifier.isbn978-3-540-76413-7en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/5479-
dc.description.abstractIn this paper, we propose an EM based Multiple-Instance learning algorithm for the image classification and indexing. To learn a desired image class, a set of exemplar images are selected by a user. Each example is labeled as conceptual related (positive) or conceptual unrelated (negative) image. A positive image consists of at least one user interested object, and a negative example should not contain any user interested object. By using the proposed learning algorithm, an image classification system can learn the user's preferred image class from the positive and negative examples. We have built a prototype system to retrieve user desired images. The experimental results show that for only a few times of relearning, a user can use the prototype system to retrieve favor images from the WWW over Internet.en_US
dc.language.isoen_USen_US
dc.subjectmultiple-instance learningen_US
dc.subjectimage retrieveen_US
dc.subjectWWWen_US
dc.titleImage classification and indexing by EM based multiple-instance learningen_US
dc.typeArticleen_US
dc.identifier.journalADVANCES IN VISUAL INFORMATION SYSTEMSen_US
dc.citation.volume4781en_US
dc.citation.spage146en_US
dc.citation.epage153en_US
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000252060300015-
Appears in Collections:Conferences Paper