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College of Engineering(공과대학)Dept. of Nuclear and Quantum Engineering(원자력및양자공학과)NE-Journal Papers(저널논문)

Distributed similarity search algorithm in distributed heterogeneous multimedia databases

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dc.contributor.authorLee, JHko
dc.contributor.authorKim, DHko
dc.contributor.authorLee, SLko
dc.contributor.authorChung, Chin-Wanko
dc.contributor.authorCha, GHko
dc.date.accessioned2007-11-14T06:52:47Z-
dc.date.available2007-11-14T06:52:47Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2000-07-
dc.identifier.citationINFORMATION PROCESSING LETTERS, v.75, no.1-2, pp.35 - 42-
dc.identifier.issn0020-0190-
dc.identifier.urihttp://hdl.handle.net/10203/1928-
dc.description.abstractThe collection fusion problem in multimedia databases is concerned with the merging of results retrieved by content based retrieval from distributed heterogeneous multimedia databases in order to find the most similar objects to a query object. We propose distributed similarity search algorithms, two heuristic algorithms and an algorithm using the linear regression, to solve this problem. To our knowledge, these algorithms are the first research results in the area of distributed content based retrieval for heterogeneous multimedia databases. (C) 2000 Elsevier Science B.V. All rights reserved.-
dc.languageEnglish-
dc.language.isoen_USen
dc.publisherELSEVIER SCIENCE BV-
dc.titleDistributed similarity search algorithm in distributed heterogeneous multimedia databases-
dc.typeArticle-
dc.identifier.wosid000088507100007-
dc.identifier.scopusid2-s2.0-0034225920-
dc.type.rimsART-
dc.citation.volume75-
dc.citation.issue1-2-
dc.citation.beginningpage35-
dc.citation.endingpage42-
dc.citation.publicationnameINFORMATION PROCESSING LETTERS-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorChung, Chin-Wan-
dc.contributor.nonIdAuthorLee, JH-
dc.contributor.nonIdAuthorKim, DH-
dc.contributor.nonIdAuthorLee, SL-
dc.contributor.nonIdAuthorCha, GH-
dc.type.journalArticleArticle-
dc.subject.keywordAuthordistributed similarity search algorithms-
dc.subject.keywordAuthorcollection fusion-
dc.subject.keywordAuthormultimedia databases-
dc.subject.keywordAuthorinformation retrieval-
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