Adaptive exploration-based whale optimization for image segmentation based on variable parametric error

Abstract

Image segmentation is the process of splitting an image into numerous segments. Its major purpose is to change or simplify the image, which could be more significant and simpler to examine. However, it does not execute well while segmenting complex images with non-homogeneous parts. In this paper, a hybrid image segmentation model with the aid of Active Contour and Graph cut techniques is proposed. Moreover, it extracts the mutual information from two adopted segmentation schemes, and subsequently, the high-intensity and low-intensity pixels of resultant images are grouped by Fuzzy Entropy Maximization (FEM) method. A modified optimization algorithm termed as Adaptive Exploration based Whale Optimization (AEW) is used for solving the FEM problem. The performance of the proposed Active contour Graph cut Fuzzy Entropy-based Segmentation(AGFES), (AEW-AGFES) is algorithmically analyzed in terms of various performance measures to substantiate its effectiveness.