Hash-based Duplicate Detection
Hash-Based Duplicate Detection is a duplicate detection method that uses hash functions to map data items into fixed-length identifiers, enabling efficient comparison and identification of duplicates by leveraging the property that identical items produce identical hash values.
64
Publications
3.8K
Citations
212
Authors
94
Institutions
Publications per year
2017–2026
21
Parents
64
Near Duplicate Image Detection: min-Hash and tf-idf Weighting
Ondřej Chum, James Philbin, Andrew Zisserman · 2008 · 470 citations · Full text
A Comprehensive Study of the Past, Present, and Future of Data Deduplication
Wen Xia, Hong Jiang, Dan Feng et al. · Proceedings of the IEEE · 2016 · 308 citations
Effective Multiple Feature Hashing for Large-Scale Near-Duplicate Video Retrieval
Jingkuan Song, Yi Yang, Zi Huang et al. · IEEE Transactions on Multimedia · 2013 · 265 citations
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Darmstadt University of Applied Sciences
Darmstadt, Germany
3 h-index
9 publications
360 citations

Cornell University
Ithaca, United States
2 h-index
3 publications
533 citations

Bond University
Gold Coast, Australia
2 h-index
2 publications
29 citations
Microsoft Research Asia (China)
Beijing, China
2 h-index
6 publications
360 citations
Fudan University
Shanghai, China
2 h-index
5 publications
140 citations
Shandong University of Finance and Economics
Jinan, China
2 h-index
5 publications
93 citations

Harbin Institute of Technology
Harbin, China
2 h-index
8 publications
624 citations
National Institute of Standards and Technology
Gaithersburg, United States
2 h-index
3 publications
100 citations
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4 publications · 589 citations
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TKDE · 2 publications · 87 citations
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1 publications · 308 citations
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TPAMI · 1 publications · 127 citations
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1 publications · 23 citations
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