Honey Badger Algorithm and Chef-based Optimization Algorithm for Multilevel Thresholding Image Segmentation

Image segmentation has an important role in image processing and computer vision and it is widely used in numerous applications, including feature extraction, pattern recognition, scene analysis, object tracking. Due to its simplicity and effectiveness, multilevel thresholding approach to image segmentation has gained increased research attention in recent years. In this paper, the ability of two recently proposed metaheuristic algorithms, Honey badger algorithm and Chef-based optimization algorithm to ascertain the optimal threshold values based on Kapur’s entropy is systematically examined. The performance of the two multilevel thresholding image segmentation methods are assessed on a dataset of nine standard benchmark images. Based on a fixed number of independent runs, for each test image and a given number of thresholds, the multilevel thresholding performance is reported using the mean and standard deviation of Kapur’s entropy as well as the best objective function value and the associated threshold values.