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dc.contributor.authorRamsey, Marshall C.
dc.contributor.authorChen, Hsinchun
dc.contributor.authorZhu, Bin
dc.date.accessioned2004-08-17T00:00:01Z
dc.date.available2010-06-18T23:45:56Z
dc.date.issued1999en_US
dc.date.submitted2004-08-17en_US
dc.identifier.citationA Collection of Visual Thesauri for Browsing Large Collections of Geographic Images 1999, 50(9):826-834 Journal of the American Society for Information Science & Technologyen_US
dc.identifier.urihttp://hdl.handle.net/10150/106407
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractDigital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely laborintensive processes for text documents and especially for images. Recently, 800,000 declassified satellite photographs were made available by the United States Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques.
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons, Inc.en_US
dc.subjectGeographic Digital Librariesen_US
dc.subjectIndexingen_US
dc.subjectDigital Librariesen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.titleA Collection of Visual Thesauri for Browsing Large Collections of Geographic Imagesen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of the American Society for Information Science & Technologyen_US
refterms.dateFOA2018-08-19T00:16:12Z
html.description.abstractDigital libraries of geo-spatial multimedia content are currently deficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge is that indexing and thesaurus creation are extremely laborintensive processes for text documents and especially for images. Recently, 800,000 declassified satellite photographs were made available by the United States Geological Survey. Additionally, millions of satellite and aerial photographs are archived in national and local map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this article we propose a scalable method to automatically generate visual thesauri of large collections of geo-spatial media using fuzzy, unsupervised machine-learning techniques.


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