Histogram Intersection Kernels

Histogram Intersection Kernels is a kernel function in machine learning that belongs to the family of positive definite kernels and is distinguished by measuring similarity between two histograms through the summation of the minima of their corresponding bin values, effectively quantifying overlap in histogram-based feature representations.

49

Publications

5.1K

Citations

137

Authors

60

Institutions

Publications per year

2017–2026

6

Authors

137

  1. JW
    Jianxin Wu

    Nanjing University, China

    3 publications · 357 citations

    3

    h-index

  2. ZL
    Zhiwu Lu

    Peking University, China

    2 publications · 41 citations

    2

    h-index

  3. KG
    Kristen Grauman

    The University of Texas at Austin, United States

    2 publications · 1.5K citations

    2

    h-index

  4. TS
    Thibaud Sénéchal

    Affectiva (United States), United States

    2 publications · 168 citations

    2

    h-index

  5. YP
    Yuxin Peng

    Peking University, China

    2 publications · 41 citations

    2

    h-index

1–5 of 137

Venues

  1. IEEE Transactions on Image Processing

    3 publications · 381 citations

    3

    h-index

  2. IEEE Transactions on Multimedia

    2 publications · 143 citations

    2

    h-index

  3. 1

    h-index

  4. International Journal of Remote Sensing

    1 publications · 49 citations

    1

    h-index

  5. Security and Communication Networks

    1 publications · 15 citations

    1

    h-index

1–5