Adaptive Exploration-based Whale Optimization for Image Segmentation Based on Variable Parametric Error
M. Jogendra Kumar1, G.V.S Raj Kumar2, K. Naveen Kumar3, Y. Srinivas4
1M. Jogendra Kumar, Department of Computer Science and Engineering, Gandhi Institute of Technology and Management, Deemed to be University, Vishakhapatnam, Andhra Pradesh, India.
2Dr. G.V.S Raj Kumar, Department of Information Technology, Gandhi Institute of Technology and Management, Deemed University, Vishakhapatnam, Andhra Pradesh, India.
3Dr. K. Naveen Kumar, Department of Information Technology, Gandhi Institute of Technology and Management, Deemed University, Vishakhapatnam, Andhra Pradesh, India.
4Dr. Y. Srinivas, Department of Information Technology, Gandhi Institute of Technology and Management, Deemed University, Vishakhapatnam, Andhra Pradesh, India.
Manuscript received on 13 April 2019 | Revised Manuscript received on 20 April 2019 | Manuscript Published on 26 July 2019 | PP: 1209-1218 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F12490486S419/19©BEIESP | DOI: 10.35940/ijitee.F1249.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Adaptiveness; Whale Optimization; Image Segmentation; Active contour; Graph cut Technique; Fuzzy Entropy Maximization Nomenclature.
Scope of the Article: Computer Science and Its Applications