Colour Image Multilevel Thresholding Segmentation Using Trees Social Relationship Algorithm

Abstract

Colour image segmentation is an essential task in image processing and computer vision that aims to divide an image into meaningful and homogeneous regions. One of the most widely used techniques for colour image segmentation is multilevel thresholding, which selects a set of optimal threshold values to separate the image pixels into different classes. However, finding the optimal thresholds is a complex and computationally intensive problem requiring efficient optimization. In this paper, we propose a novel colour image multilevel thresholding segmentation method based on the Trees Social Relationship Algorithm (TSR), a new metaheuristic algorithm inspired by the social and cooperative behaviour of trees in the forest. TSR mimics trees' hierarchical and collective life and uses four operators: growth, reproduction, competition, and death. We use TSR to optimize Kapur’s entropy as the objective function, which measures the information content of the segmented image. We compare the performance of our method with other established metaheuristic algorithms, including the Particle Swarm Optimization Algorithm (PSO), Artificial Bee Colony (ABC), Bat Optimization (BAT), Bacterial Foraging Algorithm (BFO), Backtracking Search Optimization Algorithm (BSA), Cuckoo Search (Cuckoo), Differential Evolution (DE), Electromagnetic Field Optimization (EFO), Firefly Algorithm (FA), and Wind Driven Optimization (WDO), on several benchmark colour images. We also use various evaluation metrics such as GCE, PRI, VOI, PSNR, FSIM, and SSIM to assess the quality of the segmentation results. The experimental results show that our method achieves better results than the other algorithms in accuracy, robustness, and convergence speed.