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Optimized K-means algorithm for image segmentation based on improved dung beetle algorithm.

Ning Li1, Yan Luo2, Zhiqiang Feng1

  • 1Guangxi Technological College of Machinery and Electricity, Nanning, 530007, Guangxi, China.

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Summary

This study introduces an Improved Dung Beetle Optimization (IDBO) algorithm to enhance K-means image segmentation. IDBO improves accuracy and efficiency by overcoming K-means limitations, offering better results than traditional methods.

Keywords:
Competitive mechanismCorsi inverse cumulative distributionDung beetle optimization algorithmImage segmentationK-MeansLatin hypercube samplingNonlinear decision factor

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional K-means clustering for image segmentation suffers from sensitivity to initial centers and local optima.
  • Existing optimization algorithms may lack efficiency and accuracy in complex segmentation tasks.

Purpose of the Study:

  • To propose an Improved Dung Beetle Optimization (IDBO) algorithm for enhanced K-means image segmentation.
  • To improve segmentation quality, computational efficiency, and overcome limitations of traditional K-means.

Main Methods:

  • Implemented IDBO with Latin Hypercube Sampling (LHS) for population initialization.
  • Utilized a hybrid position updating strategy balancing global exploration and local exploitation.
  • Integrated Cauchy inverse cumulative distribution and tangent flight operators for dynamic perturbation.

Main Results:

  • IDBO demonstrated superior convergence speed, accuracy, and stability on benchmark functions compared to DBO and other algorithms.
  • IDBO-optimized K-means segmentation achieved higher accuracy, better edge preservation, and improved texture fidelity (validated by MSE and PSNR).
  • Ablation studies confirmed the effectiveness of individual enhancement strategies within the IDBO framework.

Conclusions:

  • The proposed IDBO algorithm significantly enhances K-means clustering for image segmentation.
  • IDBO offers a robust and adaptive approach for high-performance image segmentation.
  • Combining intelligent optimization with clustering presents a promising direction for advanced image analysis techniques.