Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1908
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWang, Z-
dc.creatorSiu, WC-
dc.creatorFeng, DD-
dc.date.accessioned2014-12-11T08:22:28Z-
dc.date.available2014-12-11T08:22:28Z-
dc.identifier.isbn978-1-4244-2295-1-
dc.identifier.urihttp://hdl.handle.net/10397/1908-
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rights© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectContent-based retrievalen_US
dc.subjectImage classificationen_US
dc.subjectImage retrievalen_US
dc.titleImage annotation with parametric mixture model based multi-class multi-labelingen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.description.otherinformationRefereed conference paperen_US
dc.identifier.doi10.1109/MMSP.2008.4665153-
dcterms.abstractImage annotation, which labels an image with a set of semantic terms so as to bridge the semantic gap between low level features and high level semantics in visual information retrieval, is generally posed as a classification problem. Recently, multi-label classification has been investigated for image annotation since an image presents rich contents and can be associated with multiple concepts (i.e. labels). In this paper, a parametric mixture model based multi-class multi-labeling approach is proposed to tackle image annotation. Instead of building classifiers to learn individual labels exclusively, we model images with parametric mixture models so that the mixture characteristics of labels can be simultaneously exploited in both training and annotation processes. Our proposed method has been benchmarked with several state-of-the-art methods and achieved promising results.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 634-639-
dcterms.issued2008-
dc.identifier.scopus2-s2.0-58049083915-
dc.identifier.rosgroupidr42249-
dc.description.ros2008-2009 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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