Artificial Intelligence Lab, Department of MIS, University of Arizona / 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 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/106407 |
| Date | January 1999 |
| Creators | Ramsey, Marshall C., Chen, Hsinchun, Zhu, Bin |
| Publisher | John Wiley & Sons, Inc. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Journal Article (Paginated) |
Page generated in 0.0019 seconds