Manuscript received September 22, 2025; revised October 15, 2025; accepted November 21, 2025; published March 26, 2026.

Abstract—Color image segmentation is a crucial step in various image processing applications, playing a vital role in understanding image content. Nature-inspired optimization algorithms have demonstrated significant potential in enhancing segmentation performance. This paper proposes a novel color image segmentation method based on Wild Horse Optimization (WHO), enhanced with chaos theory, to avoid local optima and accelerate convergence. A logistic map is integrated into the WHO to ensure diverse population initialization and to introduce dynamic perturbations during the optimization process. The proposed method is evaluated on various color images. Metrics such as mean square error, peak signal-to-noise ratio, and structural similarity index are employed to assess the quality of the segmented images. Additionally, the best fitness values, computed using the Davies-Bouldin (DB) index, are analyzed to demonstrate the algorithm’s capacity to achieve optimal segmentation solutions. Comparative experiments with other metaheuristic segmentation methods confirm the superior effectiveness of the proposed Logistic Chaos (LC)-based WHO approach.

Keywords—image, logistic map, segmentation, Wild Horse Optimization (WHO)

Cite: Amel Tehami, Yasmina Teldja Amghar, and Mounia Hendel, "Logistic Chaos-Based Wild Horse Optimization for Enhanced Color Image Segmentation," Journal of Image and Graphics, Vol. 14, No. 2, pp. 266-267, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC-BY-4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.