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A Multi-Strategy Improved Seagull Optimization Algorithm for Global Optimization and Artistic Image Segmentation.

Yangyang Jiang1,2

  • 1Academy of Fine Arts, Chongqing University of Education, Chongqing 400067, China.

Biomimetics (Basel, Switzerland)
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Summary
This summary is machine-generated.

This study introduces an improved Seagull Optimization Algorithm (MFISOA) for efficient multilevel threshold image segmentation. MFISOA enhances optimization accuracy and stability, outperforming existing methods in complex image segmentation tasks.

Keywords:
image segmentationmulti-strategy improvednumerical optimizationseagull optimization algorithm

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Multilevel threshold image segmentation is crucial but challenged by low efficiency and poor adaptability in complex scenes.
  • Existing methods, including traditional and metaheuristic approaches, struggle with high-dimensional optimization and segmentation accuracy.
  • The standard Seagull Optimization Algorithm (SOA) exhibits limitations in exploration, local exploitation, and population diversity, hindering its effectiveness in demanding tasks.

Purpose of the Study:

  • To address the limitations of existing optimization algorithms in high-dimensional and complex image segmentation tasks.
  • To propose a novel multi-strategy fused improved Seagull Optimization Algorithm (MFISOA) for enhanced optimization performance.
  • To evaluate MFISOA's effectiveness in multilevel threshold image segmentation using benchmark datasets and metrics.

Main Methods:

  • Developed MFISOA by integrating adaptive cooperative foraging, differential evolution-driven exploitation, and centroid opposition-based boundary control.
  • Constructed a collaborative optimization framework with dynamic resource allocation, fine local search, and population diversity maintenance.
  • Validated MFISOA through numerical simulations on CEC2017 and CEC2022 benchmark suites against nine advanced algorithms.

Main Results:

  • MFISOA demonstrated superior optimization accuracy, convergence speed, and operational stability compared to nine other algorithms, with statistical significance (p < 0.05).
  • In multilevel threshold image segmentation, MFISOA achieved better performance on metrics like Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Feature Similarity Index (FSIM).
  • MFISOA enabled more accurate characterization of image grayscale distribution and produced higher-quality segmentation results across various threshold scenarios.

Conclusions:

  • MFISOA offers an efficient and reliable approach for numerical optimization problems.
  • The proposed algorithm significantly improves multilevel threshold image segmentation accuracy and quality.
  • MFISOA provides a robust solution for complex image processing tasks requiring high optimization performance.