Digital libraries of geo-spatial multimedia content are currently defficient in providing fuzzy, concept-based retrieval mechanisms to users. The main challenge being that indexing and thesaurus creation are extremely labor-intensive processes for text documents and especially for images. Currently, several terabytes of digitized satellite photographs are generated weekly by the LANDSAT and SPOT satellites. Additionally, millions of aerial photographs are archived in map libraries. Such enormous collections make human indexing and thesaurus generation methods impossible to utilize. In this paper 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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