Open AccessAdaptive Exploration-based Whale Optimization for Image Segmentation Based on Variable Parametric Error
Author(s) -
Munish Kumar,
Raj Kumar,
Kishor Kumar,
Y. Srinivas
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f1249.0486s419
Subject(s) - artificial intelligence , image segmentation , segmentation , computer science , segmentation based object categorization , pattern recognition (psychology) , entropy (arrow of time) , scale space segmentation , active contour model , pixel , computer vision , maximization , fuzzy logic , mathematics , mathematical optimization , physics , quantum mechanics
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.