Kernel-based Feature Aggregation

Kernel-Based Feature Aggregation is a feature aggregation method that leverages kernel functions to non-linearly map and combine local feature representations into a compact global descriptor by emphasizing structural similarity in a high-dimensional reproducing kernel Hilbert space.

12

Publications

3.5K

Citations

45

Authors

25

Institutions

Publications per year

2017–2026

5

Authors

45

  1. HJ
    Hervé Jeǵou

    Institut national de recherche en sciences et technologies du numérique, France

    3 publications · 3.1K citations

    3

    h-index

  2. GT
    Giorgos Tolias

    Czech Technical University in Prague, Czechia

    2 publications · 379 citations

    2

    h-index

  3. YA
    Yannis Avrithis

    National Technical University of Athens, Greece

    2 publications · 379 citations

    2

    h-index

  4. WL
    Wenyin Liu

    Guangdong University of Technology, China

    1 publications · 25 citations

    1

    h-index

  5. ZZ
    Zhen-xi Zhang

    North China Electric Power University, China

    1 publications · 19 citations

    1

    h-index

1–5 of 45

Venues

  1. Pattern Recognition

    2 publications · 59 citations

    2

    h-index

  2. International Journal of Computer Vision

    1 publications · 157 citations

    1

    h-index

  3. IEEE Transactions on Multimedia

    1 publications · 35 citations

    1

    h-index

  4. The Journal of Supercomputing

    1 publications · 225 citations

    1

    h-index

  5. 1

    h-index

1–5