An Extendible Hash for Multi-Precision Similarity Querying of Image Databases
Summary: Multi-precision similarity: partition images into parts with color histograms and 3D color vectors to prune; spatial histograms boost sub-image precision. Extendible hash index for avg col vecs; complete filtering; experiments show SR-tree gains. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Shu Lin (International Business Machines Toronto Laboratories; University of Alberta)
- 2. M. Tamer Özsu (University of Alberta; University of Waterloo)
- 3. Vincent Oria (New Jersey Institute of Technology; University of Alberta)
- 4. Raymond Ng (University of British Columbia)
BibTeX Citation
@article{lin_vldb01,
title = {{An Extendible Hash for Multi-Precision Similarity Querying of Image Databases}},
author = {Lin, Shu and Özsu, M. Tamer and Oria, Vincent and Ng, Raymond},
journal = {PVLDB},
series = {{VLDB} '01},
year = {2001}
}
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4 | The R*-tree: An Efficient and Robust Access Method for Points and Rectangles | 1990 | SIGMOD | 0.0011405675 |
| 279 | The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries | 1997 | SIGMOD | 0.00022308048 |
| 855 | Storage Mappings For Multidimensional Linear Dynamic Hashing | 1983 | PODS | 0.00013437655 |
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