Manta ray foraging optimizer-based image segmentation with a two-strategy enhancement

Image processing is an evolving field that calls for more powerful techniques to extract useful information from images. In particular, image segmentation is a preprocessing step that helps separate objects in a digital image. This article introduces an enhanced manta ray foraging optimizer (MRFO) based on two strategies - oppositional learning (OL) and vertical crossover (VC) search - for color image segmentation. This combination technique focuses on the enhancement of the explorative and exploitative cores, without compromising the computational speed. The proposed algorithm, termed OL-MRFO-VC, is integrated with Kapur entropy to identify the best threshold configuration in each image component (RGB). The technique is tested over three datasets consisting of different scenes. The threshold vector consists of both lower and higher levels in the experiments. In addition, OL-MRFO-VC is compared with fourteen competitive metaheuristics, and eleven measures are used to evaluate their performance quantitatively and qualitatively. According to the computational results, our proposed method outperforms state-of-the-art techniques, especially in the higher threshold levels. Furthermore, the p values in the Wilcoxon signed-rank test confirm a significant improvement brought by our proposed method, suggesting a superior capability of OL-MRFO-VC for solving image segmentation problems.