IDEAS home Printed from https://ideas.repec.org/a/spr/jclass/v40y2023i3d10.1007_s00357-023-09443-1.html
   My bibliography  Save this article

A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation

Author

Listed:
  • Karim Kalti

    (Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems
    University of Monastir)

  • Asma Touil

    (Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems)

Abstract

In this paper, we address the problem of the fuzzy c-means (FCM) algorithm sensitivity to noise when clustering image pixels. We propose in this regard an improved FCM algorithm that incorporates contextual information at the membership degrees updating stage. For that aim, we introduce two novel parameters: the contextual similarity degree and the intrinsic similarity degree which are used to estimate each pixel’s nature (normal or noisy), according respectively to its context and to its specific features. Based on this estimation, we propose a modified membership degrees updating strategy that proceeds by adaptively reinforcing the assignment of a pixel to its context’s cluster when this pixel is detected as noisy. Experiments performed on synthetic and real-world images proved that our approach achieves competitive performance compared to state-of-the-art FCM-based methods.

Suggested Citation

  • Karim Kalti & Asma Touil, 2023. "A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 488-512, November.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:3:d:10.1007_s00357-023-09443-1
    DOI: 10.1007/s00357-023-09443-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00357-023-09443-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00357-023-09443-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jclass:v:40:y:2023:i:3:d:10.1007_s00357-023-09443-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.