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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2074/944
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| DC Field | Value | Language |
|---|
| contributor.author | Krishnapuram, Raghu | - |
| date.accessioned | 2005-10-18T05:49:07Z | - |
| date.available | 2005-10-18T05:49:07Z | - |
| date.issued | 2001 | - |
| identifier.citation | Computer Vision and Image Understanding, 83(3), 216–235 | en |
| identifier.uri | http://eprint.iitd.ac.in/dspace/handle/2074/944 | - |
| description.abstract | In this paper, we present a robust mixture decomposition technique that automatically
finds a compact representation of the data in terms of components. We apply it to the problem of organizing databases for efficient retrieval. The time taken for
retrieval is an order of magnitude smaller than that of exhaustive search methods.We
also compare our approach with other methods for decomposition that use traditional criteria such as Akaike, Schwarz, and minimum description length.We report results on the VisTex texture image database from the MIT Media Lab. | en |
| format.extent | 987049 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | en |
| subject | categorization | en |
| subject | image databases | en |
| subject | mixture decomposition | en |
| subject | robust organization | en |
| title | Categorization of image databases for efficient retrieval using robust mixture decomposition | en |
| type | Article | en |
| Appears in Collections: | Mathematics
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Files in This Item:
| File |
Description |
Size | Format |
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| krishnapuramcat2001.pdf | | 963Kb | Adobe PDF | View/Open |
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