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Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image

by Murinto, Adhi Prahara
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 15
Year of Publication: 2019
Authors: Murinto, Adhi Prahara
10.5120/ijca2019919561

Murinto, Adhi Prahara . Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image. International Journal of Computer Applications. 177, 15 ( Nov 2019), 21-27. DOI=10.5120/ijca2019919561

@article{ 10.5120/ijca2019919561,
author = { Murinto, Adhi Prahara },
title = { Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2019 },
volume = { 177 },
number = { 15 },
month = { Nov },
year = { 2019 },
issn = { 0975-8887 },
pages = { 21-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number15/30975-2019919561/ },
doi = { 10.5120/ijca2019919561 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:45:58.693144+05:30
%A Murinto
%A Adhi Prahara
%T Multilevel Thresholding Segmentation based on Otsu’s Method and Autonomous Groups Particle Swarm Optimization for Multispectral Image
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 15
%P 21-27
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation is a process of division of images into certain regions based on certain similarities. Multispectral image consists of several bands with high dimensions, requiring a different method with the problem of low-dimensional images. Multilevel thresholding problems based on Otsu criteria are discussed in this paper. One disadvantage of the Otsu method is that computing time increases exponentially according to the number of thresholding dimensions. In this paper, the Particle Swarm Optimization (PSO) algorithm combined with the Otsu Method called multilevel thresholding Autonomous Groups Particles Swarm Optimization (MAGPSO) is proposed to reduce the two problems of PSO entrapment in the local minima and the slow rate of convergence in solving high dimensional problems. MAGPSO is used for multilevel thresholding image segmentation. The performance of MAGPSO is compared with standard PSO on three natural images. The parameters used to compare the performance of MAGPSO and PSO are the best fitness value, optimal threshold obtained from each algorithm and the measurement of the quality of segmentation results, namely: SSIM, PSNR, and MSE. From the experimental results show that MAGPSO is better when compared to PSO in image segmentation, in terms of the resulting fitness value and higher SSIM and PNSR values.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Darwinian Particle Swarm Optimization Hyperspectral Image Support Vector Machine

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