Fuzzy Multilevel Image Thresholding Based on Modified Discrete Grey Wolf Optimizer and Local Information Aggregation

Abstract

Fuzzy entropy and image thresholding are the most direct and effective methods for image segmentation. This paper, taking fuzzy Kapur's entropy as the optimal objective function, with modified discrete Grey wolf optimizer (GWO) as the tool, uses pseudotrapezoid-shaped to conduct fuzzy membership initialization so as to achieve image segmentation finally by means of local information aggregation. Experiment results show that the proposed fuzzy-based GWO and aggregation algorithm and fuzzy-based modified discrete GWO and aggregation (FMDGWOA) algorithm can search out the optimal thresholds effectively and accurately. In this paper, electro-magnetism optimization based on Kapur's entropy, standard GWO and fuzzy entropy-based differential evolution algorithm are experimentally compared with the proposed method, respectively. It shows that FMDGWOA enjoys obvious advantages in segmentation quality, objective function, and stability.