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Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image

Shikai Wang1, Kangjian Sun2, Wanying Zhang2

  • 1School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China.

Mathematical Biosciences and Engineering : MBE
|July 2, 2021
PubMed
Summary

A new Modified Ant Lion Optimizer (MALO) improves multilevel thresholding for complex image segmentation. This algorithm enhances accuracy and speed, outperforming existing methods for better region analysis.

Keywords:
Kapur's entropyOtsuant lion optimizerimage segmentationmultilevel thresholdingopposition-based learning

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Image Processing

Background:

  • Multilevel thresholding is crucial for analyzing complex images.
  • Traditional methods like Otsu and Kapur's entropy face exponential time complexity with increasing thresholds.

Purpose of the Study:

  • To develop an efficient algorithm for optimal multilevel thresholding.
  • To address the computational challenges of traditional thresholding techniques.

Main Methods:

  • A Modified Ant Lion Optimizer based on opposition-based learning (MALO) was proposed.
  • Otsu and Kapur's entropy were used as objective functions for maximization.
  • Performance was validated against IEEE CEC 2017 benchmark functions and 11 state-of-the-art algorithms.

Main Results:

  • MALO demonstrated superior performance in segmentation.
  • Improved search accuracy and convergence speed were observed due to opposition-based learning.
  • Key metrics including fitness value, PSNR, SSIM, FSIM, and computational time showed MALO's effectiveness.

Conclusions:

  • The proposed MALO algorithm is a powerful and efficient technique for multilevel thresholding.
  • MALO significantly outperforms existing methods in image segmentation tasks.
  • This method offers a robust solution for complex image region analysis.