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

Publications

5.5K

Citations

333

Authors

154

Institutions

Publications per year

2017–2026

32

Authors

333

  1. HT
    Heng Tao Shen

    University of Electronic Science and Technology of China, China

    4 publications · 660 citations

    4

    h-index

  2. ZH
    Zi Huang

    The University of Queensland, Australia

    4 publications · 660 citations

    4

    h-index

  3. HJ
    Hong Jiang

    University of Nebraska–Lincoln, United States

    3 publications · 124 citations

    3

    h-index

  4. DF
    Dan Feng

    Huazhong University of Science and Technology, China

    3 publications · 124 citations

    3

    h-index

  5. WC
    Wei-Ta Chu

    National Chung Cheng University, Taiwan

    3 publications · 56 citations

    3

    h-index

1–5 of 333

Venues

  1. IEEE Transactions on Multimedia

    4 publications · 392 citations

    4

    h-index

  2. Expert Systems with Applications

    2 publications · 76 citations

    3

    h-index

  3. IEEE Transactions on Image Processing

    3 publications · 240 citations

    3

    h-index

  4. Multimedia Tools and Applications

    3 publications · 109 citations

    3

    h-index

  5. IEEE Access

    2 publications · 76 citations

    2

    h-index

1–5