Feature-based Duplicate Detection
Feature-Based Duplicate Detection is a duplicate detection method that identifies duplicity by comparing extracted features of records, distinguished from other methods through its reliance on predefined or learned feature representations to quantify similarity.
110
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5.5K
Citations
333
Authors
154
Institutions
Publications per year
2017–2026
32
Parents
110
Multiple feature hashing for real-time large scale near-duplicate video retrieval
Jingkuan Song, Yi Yang, Zi Huang et al. · 2011 · 345 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
1–5 of 110
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University of Electronic Science and Technology of China, China
4 publications · 660 citations
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1–5 of 333
154

Huazhong University of Science and Technology
Wuhan, China
5 h-index
10 publications
320 citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
7 publications
2.3K citations

The University of Queensland
Brisbane, Australia
4 h-index
14 publications
2K citations

The University of Melbourne
Melbourne, Australia
3 h-index
9 publications
210 citations
National Chung Cheng University
Minxiong, Taiwan
3 h-index
6 publications
112 citations
Wuhan National Laboratory for Optoelectronics
Wuhan, China
3 h-index
5 publications
234 citations

Sun Yat-sen University
Guangzhou, China
3 h-index
11 publications
704 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
6 publications
195 citations
1–8 of 154
4 publications · 392 citations
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2 publications · 76 citations
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3 publications · 240 citations
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3 publications · 109 citations
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2 publications · 76 citations
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