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An efficient hybrid differential evolution-golden jackal optimization algorithm for multilevel thresholding image

Xianmeng Meng1,2, Linglong Tan1, Yueqin Wang1

  • 1School of Electronics Engineering, Anhui Xinhua University, Hefei, China.

Peerj. Computer Science
|August 15, 2024
PubMed
Summary
This summary is machine-generated.

A new hybrid algorithm, differential evolution-golden jackal optimizer (DEGJO), improves multilevel thresholding for image segmentation. This method enhances search capabilities, offering superior performance and accurate image analysis.

Keywords:
Differential evolution-golden jackal optimizationMinimum cross-entropyMultilevel thresholdingImage segmentation

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Image segmentation is vital for image analysis, with multilevel thresholding being an effective technique.
  • Increasing the number of thresholds in multilevel thresholding significantly raises computational complexity.
  • Existing methods struggle with the computational demands of complex multilevel thresholding.

Purpose of the Study:

  • To introduce a novel hybrid optimization algorithm, Differential Evolution-Golden Jackal Optimizer (DEGJO), for efficient multilevel thresholding image segmentation.
  • To enhance the search capacity of the Golden Jackal Optimizer (GJO) by integrating Differential Evolution (DE).
  • To validate the proposed DEGJO algorithm's effectiveness using the Minimum Cross-Entropy (MCE) fitness function.

Main Methods:

  • Developing a hybrid optimization algorithm (DEGJO) by combining Differential Evolution (DE) and Golden Jackal Optimizer (GJO).
  • Utilizing the Minimum Cross-Entropy (MCE) as the fitness function for image segmentation.
  • Evaluating the DEGJO algorithm on CEC2021 benchmark functions and comparing it with state-of-the-art optimization algorithms.
  • Conducting multilevel segmentation experiments on benchmark images to assess practical performance.

Main Results:

  • The DEGJO algorithm demonstrated superior performance in terms of fitness values compared to other metaheuristic algorithms on benchmark functions.
  • Experimental results on benchmark images confirmed the efficacy of DEGJO for multilevel thresholding.
  • The proposed method achieved high quantitative performance metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM).

Conclusions:

  • The DEGJO algorithm offers an effective and efficient solution for multilevel thresholding image segmentation.
  • The hybrid approach successfully enhances the optimization capabilities for complex image segmentation tasks.
  • DEGJO provides a promising tool for advanced image analysis applications requiring high segmentation accuracy.