Effective Data Co-Reduction for Multimedia Similarity Search
Summary: Data Co-Reduction (DCR) uses co-clustering to jointly compress data and features for similarity search, with lossless distance bounds. Optimal co-reduction minimizes candidates, enabling lossless retrieval and beating prior methods on high-D data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Zi Huang (University of Queensland)
- 2. Heng Tao Shen (University of Queensland)
- 3. Jiajun Liu (University of Queensland)
- 4. Xiaofang Zhou (University of Queensland)
BibTeX Citation
@inproceedings{huang_sigmod11,
title = {{Effective Data Co-Reduction for Multimedia Similarity Search}},
author = {Huang, Zi and Shen, Heng Tao and Liu, Jiajun and Zhou, Xiaofang},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989430},
url = {https://dl.acm.org/doi/10.1145/1989323.1989430},
year = {2011}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,060 | Inter-Media Hashing for Large-scale Retrieval from Heterogeneous Data Sources | 2013 | SIGMOD | 6.9322681e-05 |
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Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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