Semantic Image Segmentation Using a Hybrid Genetic–Cuckoo Search Algorithm

Abstract

Image segmentation is the process of dividing a given image into a set of regions or categories. The goal of image segmentation is to change the image representation into a form that is substantially meaningful and easy to analyze. Metaheuristic optimization algorithms are widely used algorithms for many applications among them is image segmentation. Genetic algorithm (GA) and cuckoo search (CS) algorithm are among the most popular metaheuristic algorithms. In this paper, a hybrid CS and GA (CSGA) has been used to perform image segmentation and object detection, then compared with other popular algorithms for image segmentation which are fuzzy C-mean (FCM), K-means algorithms, and GA. Simulation results of the statistical measures of the performance corroborate that CSGA outperforms other compared methods.