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Multi-Level Thresholding Color Image Segmentation Using Modified Gray Wolf Optimizer.

Pei Hu¹, Yibo Han¹, Zheng Zhang¹

¹School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China.

Biomimetics (Basel, Switzerland)

|November 26, 2024

Summary

This summary is machine-generated.

This study introduces a modified Gray Wolf Optimizer (MGWO) for efficient multi-level thresholding in color image segmentation. MGWO enhances accuracy and speed compared to traditional methods, improving segmentation results.

Keywords:

gray wolf optimizer image segmentation multi-level thresholding

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Image segmentation accuracy relies heavily on optimal threshold selection.
Multi-level thresholding offers superior detail but is computationally intensive.

Purpose of the Study:

To develop an efficient and accurate multi-level thresholding method for color image segmentation.
To enhance the Gray Wolf Optimizer (GWO) for improved thresholding performance.

Main Methods:

Modified Gray Wolf Optimizer (MGWO) incorporating enhanced leader selection, position update, and mutation strategies.
Utilized Otsu method and Kapur entropy as objective functions for threshold optimization.
Evaluated performance using metrics like PSNR, SSIM, and FSIM on the BSD500 dataset.

Main Results:

MGWO demonstrated superior performance in multi-level thresholding for color images.
Experimental and statistical analyses confirmed the effectiveness of the proposed MGWO algorithm.
Achieved excellent results in terms of objective values, variance, PSNR, SSIM, and FSIM.

Conclusions:

The modified Gray Wolf Optimizer (MGWO) provides an effective solution for accurate and efficient multi-level color image segmentation.
MGWO significantly improves upon existing methods for complex image segmentation tasks.
The enhanced algorithm offers a promising approach for advancing image processing techniques.