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Modified dragonfly algorithm based multilevel thresholding method for color images segmentation.

Xiao Xu Peng¹, He Ming Jia¹, Chun Bo Lang¹

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

Mathematical Biosciences and Engineering : MBE

|November 9, 2019

Summary

This summary is machine-generated.

A modified Dragonfly algorithm (MDA) enhances multi-level threshold segmentation for color images. This improved method achieves superior accuracy and effectiveness compared to existing algorithms, optimizing image processing.

Keywords:

Kapur's entropy Dragonfly algorithm Otsu method differential evolution elite opposition-based learning minimum cross entropy regression convolutional neural network

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Accurate image segmentation is crucial for image processing.
Multi-level thresholding segments complex images but faces computational challenges.
Existing methods struggle with efficiency and accuracy in color image segmentation.

Purpose of the Study:

To propose a modified Dragonfly algorithm (MDA) for optimal multi-level thresholding in color image segmentation.
To enhance the exploration and exploitation balance in optimization.
To improve the accuracy and computational efficiency of color image segmentation.

Main Methods:

Developed a modified Dragonfly algorithm (MDA) integrating chaotic mapping and elite opposition-based learning (EOBL).
Employed a hybrid approach combining Dragonfly Algorithm (DA) and Differential Evolution (DE) for optimization.
Utilized Kapur entropy, minimum cross-entropy, and Otsu's method as fitness functions.

Main Results:

The MDA method demonstrated superior performance across 10 test color images compared to nine other meta-heuristic algorithms.
MDA achieved better results in average fitness value (AF), standard deviation (STD), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and feature similarity index (FSIM).
Statistical tests (Friedman and Wilcoxon) confirmed significant differences in performance.

Conclusions:

The proposed MDA offers a more effective and accurate solution for color image segmentation.
MDA successfully addresses the computational complexity associated with multi-level thresholding.
This research provides a valuable advancement in automated image segmentation techniques.