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A Grouping Differential Evolution Algorithm Boosted by Attraction and Repulsion Strategies for Masi Entropy-Based

Seyed Jalaleddin Mousavirad¹, Davood Zabihzadeh¹, Diego Oliva²

¹Computer Engineering Department, Hakim Sabzevari University, Sabzevar 96179-76487, Iran.

Entropy (Basel, Switzerland)

|January 21, 2022

Summary

This summary is machine-generated.

A new algorithm, ME-GDEAR, improves multi-level image thresholding using Masi entropy. It enhances efficiency and performance by employing grouping, updating, attraction, and repulsion strategies, outperforming other methods in extensive tests.

Keywords:

clustering differential evolution image segmentation multi-level image thresholding optimisation

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Masi entropy is widely used for image thresholding.
Existing Masi entropy-based multi-level thresholding algorithms face efficiency challenges with more thresholds.

Purpose of the Study:

To propose a novel differential evolution (DE) algorithm, ME-GDEAR, for efficient Masi entropy-based multi-level image thresholding.
To enhance the efficacy of DE algorithms for this task through specific strategies.

Main Methods:

Developed the ME-GDEAR algorithm, a population-based metaheuristic.
Incorporated a grouping strategy using a clustering algorithm to partition the population.
Implemented an updating strategy to integrate clusters into the population.
Enhanced the algorithm with attraction (to the best individual) and repulsion (from random individuals) strategies.

Main Results:

ME-GDEAR demonstrated excellent image thresholding performance on benchmark images.
Outperformed other metaheuristics in 37/48 cases (cost function), 26/48 cases (feature similarity index), and 20/32 cases (Dice similarity).

Conclusions:

Population-based metaheuristics are effective for entropy-based image thresholding.
Balancing exploitation and exploration strategies, as done in ME-GDEAR, is vital for algorithm design.