Color Image Multi-Threshold Segmentation Based on Modified Reptile Search Algorithm

Multi-threshold image segmentation is a common technique in computer vision and image analysis. However, segmentation quality suffers greatly as the number of thresholds increases, particularly for color image segmentation tasks. To address this challenge, this paper proposes a modified reptile search algorithm (MRSA) based on Otsu and Kapur objective functions. Firstly, an RSA algorithm is developed by combining an adaptive weight factor and elite-guided learning to improve segmentation performance. Secondly, an RGB channel symmetric cooperation mechanism is introduced to exchange information among color channels. Thirdly, a repair mechanism is designed to maintain the structural symmetry of solutions throughout the optimization process. We conduct extensive experiments on the BSD500 benchmark color images under different threshold levels and compare MRSA with an improved bald eagle search algorithm (IBES), enhanced Giza pyramids construction algorithm (GGPC), multi-mechanism artificial lemming algorithm (MALA), and RSA. The experimental results demonstrate that the proposed MRSA algorithm achieves superior segmentation performance in terms of objective function values, region covering, peak signal-to-noise ratio, structural similarity index measure, and feature similarity index, and it exhibits excellent results even at high threshold levels.