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Multi-strategy remora optimization algorithm for color multi-threshold image segmentation.

Heming Jia¹, Changsheng Wen², Honghua Rao³

¹School of Information Engineering, Sanming University, Sanming, Fujian, China.

|February 18, 2026

Summary

This summary is machine-generated.

A new Multi-Strategy Remora Optimization Algorithm (MSROA) enhances color image segmentation by preventing local optima and improving convergence. This method achieves superior segmentation accuracy and image quality compared to existing algorithms.

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Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Multi-threshold image segmentation is crucial but computationally complex due to large search spaces.
Existing optimization algorithms often suffer from local optima and slow convergence.

Purpose of the Study:

To introduce the Multi-Strategy Remora Optimization Algorithm (MSROA) for efficient and accurate color image segmentation.
To enhance optimization performance by preventing local optima and accelerating convergence.

Main Methods:

MSROA integrates Beta random restarts with a "prior" property to avoid local optima.
A random walk with fast predation and elite learning strategies are employed to boost convergence speed and accuracy.
Performance evaluated on CEC2017 and CEC2020 benchmark suites and applied to Otsu's and Kapur's methods for image segmentation.

Main Results:

MSROA demonstrated statistically significant improvements over seven state-of-the-art algorithms via Wilcoxon rank-sum tests.
The algorithm accurately identified optimal threshold combinations, producing higher quality segmented images.
Consistently higher PSNR, FSIM, and SSIM values indicate superior preservation of image details.

Conclusions:

MSROA offers a robust and efficient solution for multi-threshold color image segmentation.
The algorithm effectively balances exploration and exploitation for improved optimization.
MSROA outperforms existing methods in segmentation accuracy and detail preservation.