Unsupervised Learning For Segmentation

Unsupervised Learning for Segmentation is a machine learning approach that partitions data into distinct groups or regions based on inherent patterns and similarities without relying on labeled training examples, thereby distinguishing itself from supervised segmentation methods by eliminating the need for prior ground-truth annotations.

305

Publications

21.2K

Citations

885

Authors

396

Institutions

Publications per year

2017–2026

98

Authors

885

  1. WP
    Wojciech Pieczynski

    Centre National de la Recherche Scientifique, France

    10 publications · 545 citations

    10

    h-index

  2. LH
    Lawrence Hall

    University of South Florida, United States

    5 publications · 644 citations

    5

    h-index

  3. LJ
    Licheng Jiao

    Xidian University, China

    5 publications · 224 citations

    5

    h-index

  4. JC
    James C. Bezdek

    University of West Florida, United States

    4 publications · 741 citations

    4

    h-index

  5. JL
    Jérôme Lapuyade‐Lahorgue

    Centre National de la Recherche Scientifique, France

    3 publications · 110 citations

    3

    h-index

1–5 of 885

Venues

  1. Pattern Recognition

    14 publications · 1.4K citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 11 publications · 406 citations

    11

    h-index

  3. Signal Processing

    7 publications · 252 citations

    7

    h-index

  4. IEEE Transactions on Image Processing

    7 publications · 690 citations

    7

    h-index

  5. Applied Soft Computing

    7 publications · 515 citations

    7

    h-index

1–5