BibSonomyburst/user/paregorios/imagesBibSonomy RSS feed for /user/paregorios/images2012-02-16T14:05:05+01:00http://www.bibsonomy.org/bibtex/20b3a6e647154a385082389038a107820/paregoriosparegorios2008-11-07T17:59:33+01:00commons flickr images <span class="authorEditorList"><a href="/author/Bernstein">Shelley Bernstein</a> </span>(<em>November 2008</em>)Fri Nov 07 17:59:33 CET 2008Brooklyn Museum: Community: bloggers@brooklynmuseumNovemberFlickr Commons: It's Complicated2008commons flickr images http://www.bibsonomy.org/bibtex/268cd4caadf07ac565f966e7e2e0dc572/paregoriosparegorios2008-08-29T17:52:45+02:00geography images thesauri <span class="authorEditorList"><a href="/author/Ramsey">Marshall C. Ramsey</a>, <a href="/author/Chen">Hsinchun Chen</a>, <a href="/author/Zhu">Bin Zhu</a>, and <a href="/author/Schatz">Bruce R. Schatz</a> </span><em>Journal of the American Society for Information Science</em> (<em>2000</em>)Fri Aug 29 17:52:45 CEST 2008Journal of the American Society for Information Science826-834A collection of visual thesauri for browsing large collections of geographic images502000geography images thesauri Digital 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 labor-intensive 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.