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Related Experiment Video

Updated: Aug 19, 2025

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Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability

Waleed Alomoush1, Osama A Khashan2, Ayat Alrosan1

  • 1School of Information Technology, Skyline University College, Sharjah P.O. Box 1797, United Arab Emirates.

Sensors (Basel, Switzerland)
|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel approach to improve fuzzy C-means (FCM) clustering for image segmentation. The enhanced method, PIABC-FCM, overcomes limitations like local optima and noise sensitivity, offering more accurate grayscale image segmentation.

Keywords:
artificial bee colonycentroids locationdata clusteringnatural images and validity index

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Fuzzy C-means (FCM) is a widely used soft segmentation technique for image analysis.
  • FCM's effectiveness is hindered by sensitivity to initial cluster centers, noise, and a tendency to converge to local optima.

Purpose of the Study:

  • To address the limitations of traditional FCM clustering algorithms.
  • To enhance the exploration and exploitation balance in FCM for improved image segmentation.

Main Methods:

  • A two-phase approach integrating an improved global best-guided artificial bee colony algorithm (IABC) with a new search probability model (PIABC).
  • The PIABC algorithm balances exploration and exploitation to refine FCM's initial cluster center selection.
  • The proposed PIABC-FCM method utilizes PIABC's balancing capabilities to avoid local optima during FCM clustering.

Main Results:

  • The PIABC-FCM algorithm demonstrated improved performance in grayscale image segmentation.
  • The proposed method showed promising results compared to existing related works, indicating enhanced accuracy and robustness.

Conclusions:

  • The PIABC-FCM approach effectively mitigates the local optima and noise sensitivity issues inherent in FCM.
  • This novel method offers a robust and accurate solution for grayscale image segmentation, outperforming conventional techniques.