CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Murinto, Adhi Prahara
journal cover thumbnail

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
  1. Otsu,N. (1979). A threshold selection method form gray-level histogram. IEEE Trans.Syst.Man.Cybern, Vol.SMC-9 no.1 pp62-66, Jan 1979.
  2. Kapur, J. N., Sahoo, P. K., & Wong, A. “A new method for gray-level picture thresholding using the entropy of
  3. The histogram”, 1985, Computer vision, graphics, and image processing, 29(3), 273-285.
  4. Yin, P. Y., & Chen, L. H, “A fast iterative scheme for multilevel thresholding methods”, 1997, Signal processing, 60(3), 305-313.
  5. Sezgin, M and Sankur, B., “Survey over image thresholdingtechniques and quantitative performance evaluation”, 2004, Journal of Electronic Imaging, vol. 13, no. 1, pp. 146–168.
  6. Sahoo, L., Banerjee, A., Bhunia, A. K., & Chattopadhyay, S, “An efficient GA–PSO approach for solving mixed-integer nonlinear programming problem in reliability optimization”, 2014, Swarm and Evolutionary Computation, 19, 43-51.
  7. Priya, M. S., & Nawaz, D. G. K, “Multilevel Image Thresholding using Otsu Algorithm in Image Segmentation”, 2017, International Journal of Scientific & Engineering Research, 8(5).
  8. Suresh, S., & Lal, S, “Multilevel thresholding based on Chaotic Darwinian Particle Swarm Optimization for segmentation of satellite images”, 2017, Applied Soft Computing, 55, 503-522.
  9. Talukder, S, “Mathematicle modelling and applications of particle swarm optimization”, 2010 Thesis, Blekinge Institute of Technology.
  10. Maitra, M., & Chatterjee, A, “A hybrid cooperative–comprehensive learning based PSO algorithm for image segmentation using multilevel thresholding”, 2008, Expert Systems with Applications, 34(2), 1341-1350.
  11. Mirjalili, S., Lewis, A. & Sadiq, A.S. Arab J Sci Eng, “Autonomous Partilce Groups for Particle Swarm Optimization”, 2014, 39: 4683.
  12. Eberhart, R., & Kennedy, J.”A new optimizer using particle swarm theory. In Micro Machine and Human Science, 1995. MHS'95.Proceedings of the Sixth International Symposium on (pp. 39-43). IEEE.
  13. Akay, B, “A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding”, 2013, Applied Soft Computing Journal, vol. 3, no. 6, pp. 3066–3091.
  14. Murinto, M., Astuti, N. R. D. P., & Mardhia, M. M. “Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods”, 2019, International Journal of Advances in Intelligent Informatics, 5(1), 66-75.
Index Terms

Computer Science
Information Sciences

Keywords

Darwinian Particle Swarm Optimization Hyperspectral Image Support Vector Machine

Powered by PhDFocusTM