Welcome to the InfoSci Platform
Reference Hub4
Elephant Herding Optimization for Multi-Level Image Thresholding

Elephant Herding Optimization for Multi-Level Image Thresholding

Falguni Chakraborty, Provas Kumar Roy, Debashis Nandi
Copyright: © 2020 |Volume: 11 |Issue: 4 |Pages: 27
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799802877|DOI: 10.4018/IJAMC.2020100104
Cite Article Cite Article

MLA

Chakraborty, Falguni, et al. "Elephant Herding Optimization for Multi-Level Image Thresholding." IJAMC vol.11, no.4 2020: pp.64-90. http://doi.org/10.4018/IJAMC.2020100104

APA

Chakraborty, F., Roy, P. K., & Nandi, D. (2020). Elephant Herding Optimization for Multi-Level Image Thresholding. International Journal of Applied Metaheuristic Computing (IJAMC), 11(4), 64-90. http://doi.org/10.4018/IJAMC.2020100104

Chicago

Chakraborty, Falguni, Provas Kumar Roy, and Debashis Nandi. "Elephant Herding Optimization for Multi-Level Image Thresholding," International Journal of Applied Metaheuristic Computing (IJAMC) 11, no.4: 64-90. http://doi.org/10.4018/IJAMC.2020100104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.