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)

Paper ID: 8937
Venue: VLDB
Year: 2001
Pagerank: 5.093636e-05
Overall Rank: 12,864 | 11.75%
DOI: -

Incoming Non-self Citations Over Time

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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}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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	0.001157935
277	The SR-tree: An Index Structure for High-Dimensional Nearest Neighbor Queries	1997	SIGMOD	0.00022537944
833	Storage Mappings For Multidimensional Linear Dynamic Hashing	1983	PODS	0.0001372601

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6	12,944	Algorithms for Querying by Spatial Structure	1998	VLDB
7	108	Optimizing Multi-Feature Queries for Image Databases	2000	VLDB
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