Multi-level Image Segmentation using Randomized Spiral-based Whale Optimization Algorithm

Author(s): Basu Dev Shivahare*, S.K. Gupta

Journal Name: Recent Patents on Engineering

Volume 15 , Issue 5 , 2021

Article ID: e290621184383
Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Background: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is a challenging problem.

Objective: This paper introduces a novel multi-level thresholding based image segmentation method.

Methods: The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analyze the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods.

Results: The segmentation results are validated in terms of subjective and objective evaluation.

Conclusion: Experiments arm that the presented method is efficient and competitive than the existing multi-level image segmentation methods.

Keywords: Multi-level thresholding, whale optimization algorithm, image segmentation, swarm intelligence, optimization, berkeley.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2021
Published on: 30 July, 2020
Article ID: e290621184383
Pages: 13
DOI: 10.2174/1872212114999200730163151
Price: $25

Article Metrics

PDF: 344