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A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image

Bowen Wu¹, Liangkuan Zhu¹, Jun Cao¹

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

Entropy (Basel, Switzerland)

|December 24, 2021

Summary

This summary is machine-generated.

A new Hybrid Preaching Optimization Algorithm (HPOA) enhances color image segmentation by balancing exploration and exploitation. This method achieves superior multilevel thresholding segmentation performance on benchmark and real-world image datasets.

Keywords:

Kapur entropy color image segmentation distributed time-delay evolutionary state heuristic algorithm preaching optimization algorithm

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Multilevel thresholding is crucial for image segmentation across various applications.
Determining optimal image thresholds is a challenging computational problem.

Purpose of the Study:

To introduce a novel Hybrid Preaching Optimization Algorithm (HPOA) for enhanced color image segmentation.
To improve the accuracy and efficiency of multilevel thresholding techniques.

Main Methods:

Developed HPOA incorporating an evolutionary state strategy for balanced exploration-exploitation.
Introduced a time-delay mechanism inspired by particle swarm optimization to prevent premature convergence.
Evaluated HPOA on benchmark functions and compared it against seven state-of-the-art algorithms.

Main Results:

HPOA demonstrated superior performance in accuracy, convergence, and statistical analysis compared to existing methods.
Experiments on Berkeley image datasets showed significant improvements in segmentation quality.
Key metrics like FSIM, PSNR, SSIM, and Kapur entropy confirmed the effectiveness of HPOA.

Conclusions:

The proposed HPOA offers a robust and effective solution for multilevel thresholding color image segmentation.
HPOA exhibits remarkable and promising performance, outperforming other algorithms.
This method holds significant potential for advancing image segmentation applications.