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K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm.

Donglin Zhu1,2, Linpeng Xie1, Changjun Zhou1

  • 1College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China.

Computational Intelligence and Neuroscience
|March 28, 2022
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Summary
This summary is machine-generated.

This study introduces an improved manta ray foraging optimization (IMRFO) to enhance K-means image segmentation, overcoming local optimum issues for better stability and accuracy in image analysis.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Traditional K-means image segmentation suffers from randomness and local optima, reducing segmentation quality.
  • Existing methods struggle to balance global and local optimization effectively.

Purpose of the Study:

  • To propose an improved manta ray foraging optimization (IMRFO) method for K-means image segmentation.
  • To enhance the stability and accuracy of K-means by addressing its inherent limitations.

Main Methods:

  • Incorporating Lévy flight and random walk learning into manta ray foraging optimization.
  • Integrating particle swarm optimization concepts to improve convergence accuracy.
  • Evaluating IMRFO against 11 other algorithms on standard test functions and underwater images.

Main Results:

  • IMRFO demonstrated superior optimization ability compared to 7 basic and 4 variant algorithms on test functions.
  • Experiments on underwater images showed improved Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) using IMRFO.
  • The optimized K-means method exhibited enhanced stability and performance.

Conclusions:

  • The proposed IMRFO significantly improves K-means image segmentation by enhancing global and local optimization capabilities.
  • IMRFO offers a more robust and accurate approach to image segmentation, particularly for complex datasets like underwater imagery.