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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorWang, Z-
dc.creatorGuan, G-
dc.creatorWang, J-
dc.creatorFeng, DD-
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.subjectGaussian processesen_US
dc.subjectContent-based retrievalen_US
dc.subjectMultimedia systemsen_US
dc.subjectSearch enginesen_US
dc.subjectSemantic Weben_US
dc.subjectVideo retrievalen_US
dc.titleMeasuring semantic similarity between concepts in visual domainen_US
dc.typeConference Paperen_US
dc.description.otherinformationAuthor name used in this publication: Dagan Fengen_US
dc.description.otherinformationRefereed conference paperen_US
dcterms.abstractConcept similarity has been intensively researched in the natural language processing domain due to its important role in many applications such as language modeling and information retrieval. There are few studies on measuring concept similarity in visual domain, though concept based multimedia information retrieval has attracted a lot of attentions. In this paper, we present a scalable framework for such a purpose, which is different from traditional approaches to exploring correlation among concepts in image/video annotation domain. For each concept, a model based on feature distribution is built using sample images collected from the Internet. And similarity between concepts is measured with the similarity between their models. Hereby, a Gaussian Mixture Model (GMM) is employed to model each concept and two similarity measurements are investigated. Experimental results on 13,974 images of 16 concepts collected through image search engines have demonstrated that the similarity between concepts is very close to human perception. In addition, the entropy of GMM cluster distributions can be a good indication of selecting concepts for image/video annotation.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing : 8-10 October, 2008, Cairns, Australia, p. 628-633-
dc.description.ros2008-2009 > Academic research: refereed > Refereed conference paper-
dc.description.oaVersion of Recorden_US
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