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Related Experiment Video

Updated: Nov 27, 2025

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Kapur's Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm.

Chunbo Lang1, Heming Jia1

  • 1College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

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A novel hybrid whale optimization algorithm (WOA-DE) enhances image segmentation by balancing exploration and exploitation. This new method, WOA-DE, shows superior performance compared to existing algorithms for multilevel color image segmentation tasks.

Area of Science:

  • Computational intelligence
  • Image processing
  • Optimization algorithms

Background:

  • Image segmentation is crucial for image analysis but presents optimization challenges.
  • Existing meta-heuristic algorithms may struggle with balancing exploration and exploitation phases.
  • Multilevel color image segmentation requires robust and efficient optimization techniques.

Purpose of the Study:

  • To introduce a new hybrid whale optimization algorithm (WOA-DE) for improved optimization.
  • To enhance the exploitation capability of the whale optimization algorithm (WOA) using differential evolution (DE).
  • To apply the proposed WOA-DE algorithm to the complex problem of multilevel color image segmentation.

Main Methods:

  • Development of the hybrid whale optimization algorithm with differential evolution (WOA-DE).
Keywords:
Kapur’s entropyOtsu methodcolor image segmentationdifferential evolutionhybrid algorithmwhale optimization algorithm

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  • Utilization of Kapur's entropy for efficient image segmentation.
  • Experimental evaluation on diverse image datasets (natural, satellite, MR).
  • Comparison with state-of-the-art meta-heuristic and conventional methods.

    • Main Results:

    • The WOA-DE algorithm demonstrated superior performance in multilevel color image segmentation.
    • Quantitative analysis using metrics like PSNR, SSIM, and FSIM confirmed effectiveness.
    • Statistical tests (Wilcoxon, Friedman) supported the superiority of WOA-DE over other algorithms.
    • The proposed method outperformed the Otsu method in segmentation tasks.

    Conclusions:

    • The proposed WOA-DE algorithm effectively balances exploitation and exploration for optimization tasks.
    • WOA-DE offers a significant improvement for multilevel color image segmentation.
    • The hybrid approach provides a robust and efficient solution for complex image analysis problems.