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Related Experiment Videos

Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure.

Songcan Chen1, Daoqiang Zhang

  • 1Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016 PRC. s.chen@nuaa.edu.cn

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2004
PubMed
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This study introduces robust kernelized fuzzy c-means clustering algorithms (KFCM) to improve image segmentation. These methods enhance noise and outlier robustness, effectively revealing non-Euclidean data structures.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Data Mining

Background:

  • Fuzzy c-means clustering with spatial constraints (FCM_S) is effective for image segmentation.
  • FCM_S uses fuzziness and spatial information but is sensitive to noise and outliers.
  • It struggles with non-Euclidean data structures due to Euclidean distance (L2 norm).

Purpose of the Study:

  • To develop robust kernelized variants of FCM_S (KFCM_S, KFCM_S1, KFCM_S2).
  • To enhance robustness against noise and outliers.
  • To enable clustering of non-Euclidean data structures.

Main Methods:

  • Proposed two simplified FCM_S variants (FCM_S1, FCM_S2).
  • Extended FCM_S and its variants using kernel methods to create robust versions (KFCM_S, KFCM_S1, KFCM_S2).

Related Experiment Videos

  • Introduced robust non-Euclidean distance measures and derived new objective functions.

    • Main Results:

    • The proposed kernelized algorithms demonstrate enhanced robustness to noise and outliers.
    • KFCM algorithms effectively cluster non-Euclidean data structures.
    • Experiments show improved performance on artificial and real-world datasets, especially with spatial constraints.

    Conclusions:

    • Kernelized fuzzy c-means clustering with spatial constraints offers superior robustness and data structure analysis.
    • These methods provide a computationally simple yet effective approach for advanced image segmentation and data clustering.