D-MOSG: Discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding

Abstract

Segmentation is an important step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is a very popular approach. To apply the thresholding approach, many methods such as Otsu, Kapur, Renyi etc. have been proposed in order to produce the thresholds that will segment the image optimally. These suggested methods usually have their own characteristics and are successful for particular images. It can be thought that better results may be obtained by using objective functions with different characteristics together. In this study, the thresholding which is originally applied as a single-objective problem has been considered as a multi-objective problem by using the Otsu and Kapur methods. Therefore, the discrete multi-objective shuffled gray wolf optimizer (D-MOSG) algorithm has been proposed for multi-level thresholding segmentation. Experiments have clearly shown that the D-MOSG algorithm has achieved superior results than the compared algorithms.