Content-based Image Retrieval

Content-Based Image Retrieval is a computational framework in digital image processing that enables image search and retrieval based on low-level visual features such as color, texture, and shape, rather than textual annotations or metadata.

3.1K

Publications

183.3K

Citations

6.5K

Authors

1.8K

Institutions

Publications per year

2017–2026

570

Variants

Cbir

Authors

6.5K

  1. TS
    Thomas S. Huang

    University of Illinois Urbana-Champaign, United States

    26 publications · 6.3K citations

    22

    h-index

  2. GL
    Guojun Lu

    Monash University, Australia

    19 publications · 3.4K citations

    18

    h-index

  3. RD
    Ricardo da Silva Torres

    Universidade Estadual de Campinas (UNICAMP), Brazil

    21 publications · 1.3K citations

    18

    h-index

  4. SA
    Sameer Antani

    United States National Library of Medicine, United States

    31 publications · 2K citations

    17

    h-index

  5. HM
    Henning Müller

    HES-SO University of Applied Sciences and Arts Western Switzerland, Switzerland

    20 publications · 1.4K citations

    17

    h-index

1–5 of 6.5K

Venues

  1. Pattern Recognition

    58 publications · 6.5K citations

    38

    h-index

  2. IEEE Transactions on Image Processing

    52 publications · 6.5K citations

    37

    h-index

  3. 36

    h-index

  4. Multimedia Tools and Applications

    80 publications · 2.6K citations

    29

    h-index

  5. 25

    h-index

1–5