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Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation

by Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 49
Year of Publication: 2023
Authors: Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu
10.5120/ijca2023922595

Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu . Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation. International Journal of Computer Applications. 184, 49 ( Mar 2023), 1-12. DOI=10.5120/ijca2023922595

@article{ 10.5120/ijca2023922595,
author = { Ntaye Emmanuel, Michael Asante, Dennis Redeemer Korda, Emmanuel Oteng Dapaah, Dickson Kodzo Mawuli Hodowu },
title = { Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2023 },
volume = { 184 },
number = { 49 },
month = { Mar },
year = { 2023 },
issn = { 0975-8887 },
pages = { 1-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number49/32632-2023922595/ },
doi = { 10.5120/ijca2023922595 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:20.583938+05:30
%A Ntaye Emmanuel
%A Michael Asante
%A Dennis Redeemer Korda
%A Emmanuel Oteng Dapaah
%A Dickson Kodzo Mawuli Hodowu
%T Improved Grey Wolf Optimizer based on Levy Flight for Multi-thresholding Image Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 49
%P 1-12
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Gray Wolf Optimizer is a relatively new and efficient population-based optimizer that seeks to speed up computations and find optimal solution for image segmentation problems. It is a metaheuristic algorithm that mimics the social hierarchy and hunting behavour of the gray wolfs. However, because of the insufficient diversity wolves in some cases, it is still prone to stagnation at a local optimum. This may often happen when the GWO is not able to perform a smooth transaction from exploration to exploitation potential by more iteration. This paper proposed an improved gray wolf optimizer for Multilevel image segmentation based on levy flight (LGWO). Levy flight is an efficient strategy that increase the population diversity and prevents premature convergence by improving the ability to jump out of a local optimum. The performance of the LGWO is than evaluated and compared with two conventional population-based algorithms, the Particle Swarm Optimizer (PSO) and the Bat Algorithm (BA) by using the Kapur’s entropy and Otsu’s between-class variance function with ten standard gray scale images in a multi-threshold problem. The quality of the segmented images is compared using the maximum objective function, peak signal- to noise ratio (PSNR), CPU computation time and the optimal threshold value. The experimental results proved the LGWO algorithm an efficient and reliable algorithm in solving continuous image segmentation problems.

References
  1. X. Y. Yang, "A new metaheuristic bat-inspired Algorithm," Studies in Computational Intelligence,, vol. 284, pp. pp. 65–74, 2010, 2010.
  2. A. Alihodzic and M. Tuba, "Improved hybridized bat algorithm for global numerical optimization," in Proceedings of the 16th IEEE International Conference on Computer Modelling and Simulation, UKSim-AMSS '14, March,2014.
  3. X.-S. Yang, "Firefly algorithms for multimodal optimization,” in Stochastic Algorithms," Foundations and Applications, vol. 5792 of Lecture Notes in Computer Science, p. 169–178, 2009.
  4. I. Fister,, I. J. Fister , X. S. Yang, and J. Brest, "A comprehensive review of firefly algorithms," Swarm and Evolutionary Compu tation, vol. 13, no. 1, p. 34–46, 2013.
  5. R. R. Jovanovic and M. Tuba, "Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem," Computer Science and Information Systems, vol. 10, no. 1, pp. 133 - 149, 2013.
  6. M. M. Dorigo and L. M. Gambardella, "Ant colonies for the travelling salesman problem," Biosystems, vol. 43, no. 2, pp. 73 - 81, 1997.
  7. S. Mirjalili, S. M. Mirjalili and A. Lewis, "Grey Wolf Optimizer," Advance Engineering Software, vol. 69, pp. 46 - 61, 2014.
  8. S. Mirjalili, "How effective is the Grey Wolf Optimizer in training multi-later perceptrons," Applied intelligence, vol. 43, no. 1, pp. 150 - 161, 2015.
  9. Eberhart, R; Kennedy, J;, A new optimizer using particle swarm theory, Proc.Sixth Int.Symp.Micro Mach.Hum.Sci, 1995, p. 39 43.
  10. Sathya, P D; Kayalvizhi, R;, Modified bacteria foraging for image segmentation, 2011.
  11. J. L. M. ´. ´. J. A. A. A. a. C. C. J. Lazaro, "Neuro semantic thresholding using OCR software for high precision OCR applications," Image and Vision Computing, vol. 28, no. 4, p. 71–578, 2010.
  12. C.-L. C. Y.-L. L. a. J. J. Y.-T. Hsiao, "Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames," Image and Vision computing , vol. 24, no. 10, pp. 1123 - 1136, 2006..
  13. M. Y. M. H. R. a. N. H. H. R. Adollah, "Multilevel thresholding as a simple segmentation technique in acute leukemia images," Journal of Medical Imaging and Health informatics, vol. 2, no. 3, pp. 285 - 288, 2012..
  14. G. C. Anagnostopoulos, "SVM-based target recognition from synthetic aperture radar images using target region outline descriptors," Nonlinear Analysis: Theory, Methods and Applications, vol. 71, no. 12, p. e2934–e2939, 2009..
  15. D. K. M. Hodowu, D. R. Korda and E. Ansong, "An Enhancement of Data Security in Cloud Computing with an Implementation of a Two-Level Cryptographic Technique, using AES and ECC Algorithm," International Journal of Engineering Research & Technology, vol. 09, no. 09, 2020.
  16. D. R. Korda, E. Ansong and D. K. M. Hodowu, "Securing Data in the Cloud using the SDC Algorithm," International Journal of Computer Applications, vol. 183, no. 25, pp. 24-29, 2021.
  17. P. K. Sahoo, S. Soltani and A. . K. Wong, " A survey of thresholding techniques," Copmuter Vision, Graphics, and Image Processing, vol. 41, no. 2, pp. 233-260, 1988.
  18. N. R. Pal and S. K. Pal, "Parttern Recognition," Expert Systems with Applications, vol. 26, no. 9, p. 1274–1294., 1993.
  19. S. Ouadfel and A. Taleb-ahmed, "Social spiders optimization and flower pollination algorithm for multilevel image thresholding : A performance study," Expert Systems With Applications, vol. 55, p. 566–584, 2016.
  20. J. L. M. J. A. A. A. a. C. C. J. Lázaro, "Neuro semantic thresholding using OCR software for high precision OCR application," Image and Vision Computing, vol. 28, no. 4, pp. 571 - 578, Apr. 2010.
  21. G. C. Anagnostopoulos, "SVM-based target recognition from synthetic aperture radar images using target region outline descriptors. Nonlinear Analysis, Theory, Methods and Applications," Nonlinear Analysis: Theory, Methods & Applications, vol. 71, no. 12, p. e2934–e2939, 2009a.
  22. C. L. C. Y. L. L. a. J. A. J. Y. T. Hsiao, "Robust multiple objects tracking using image segmentation and trajectory estimation scheme in video frames," Image and Vision Computing, vol. 24, no. 10, p. 1123–1136, Oct. 2006.
  23. M. Y. M. H. R. a. N. H. H. R. Adollah, "Multilevel Thresholding as a Simple Segmentation Technique in Acute Leukemia Images," Journal of Medical Imaging and Health Informatics, vol. 2, no. 3, p. 285–288, Sep 2012.
  24. A. &. N. A. K. Rojas Domínguez, "Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection," Computerized Medical Imaging and Graphics, vol. 32, no. 4, p. 304–315., 2008.
  25. A. A. a. M. Tuba, "Improved Bat Algorithm Applied to Multilevel Image Thresholding," Scientific World Journa, vol. 2014, pp. 1 -16, 2014.
  26. S. K. P. S. T. K. &. P. M. Kumar, "Bi-level thresholding using PSO, Artificial Bee Colony and MRLDE embedded with Otsu method," Memetic Computing, vol. 5, no. 4, p. 323–334, 2013.
  27. Y. z. Z. W. x. J. P. p. M. C. h. &. Y. J. j. Guo, "Multi-object extraction from topview group-housed pig images based on adaptive partitioning and multilevel thresholding segmentation," Biosystems Engineering, vol. 135, p. 54–60, 2015.
  28. S. K. A. B. V. &. S. G. K. Pare, "A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve," Applied Soft Computing Journa, vol. 1, no. 47, p. 76–102, 2016.
  29. N. R. P. a. S. K. Pa, "A review on image segmentation techniques," Pattern Recognition,, vol. 26, no. 9, p. 1277–1294, Sep.1993.
  30. E. C. a. H. S. V. Osuna-Enciso, "A comparison of nature inspired algorithms for multi-threshold image segmentation," Expert Systems with Applications, vol. 40, no. 4, pp. 1213 - 1219, Mar,2013.
  31. S. M. M. a. A. L. S. Mirjalili, "Grey Wolf Optimizer," Advances in Engineering Software, vol. 69, p. 46–61, Mar. 2014.
  32. A. A. &. P. P. Heidari, " An efficient modified grey wolf optimizer with Lévy flight for optimization tasks," Applied Soft Computing Journal, vol. 60, no. july, p. 115–134, 2017.
  33. C. Z. X. R. G. A. H. a. K. M. Yang X.-S., "Swarm Intelligence and Bio-Inspired Computation: Theory and Applications - .," Google Books, 2013.
  34. M. W. W. J. S. Z. L. F. G. S. S. &. X. W. Guo, " An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems," IEEE, vol. pp, 2020.
  35. H. &. C. S. Yang, "Incorporating a multi-criteria decision procedure into the combined dynamic programming/production simulation algorithm for generation expansion planning," IEEE Transactions on Power Systems, vol. 4, no. 1, pp. 165-175, 1989.
  36. S. S. R. S. F. &. K. H. R. Reddi, "Multilevel thresholding for image segmentation through a fast statistical recursive algorithm," ScienceDirect, vol. 4, p. 661–665, 1984.
  37. T. L. T. K. A. J. &. R. C. R. Da Silveira, "Automated drowsiness detection through wavelet packet analysis of a single EEG channel," Expert Systems with Applications, vol. 55, p. 559–565, 2016.
  38. N. Otsu, "A threshold selection method from gray level histograms," IEEE Transaction on Systems, vol. , pp. 62 - 66, 1979.
  39. J. N. S. P. K. &. W. A. K. C. Kapur, "A new method for gray-level picture thresholding using the entropy of the histogram," Computer Vision, Graphics, & Image Processing, vol. 29, no. 3, p. 273–285, 1985.
  40. C. Z. X. R. G. A. H. a. K. M. Yang X.-S., Swarm Intelligence and Bio-Inspired Computation: Theory and Applications - ., Google Books, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Gray Wolf Optimizer Optimization Lévy Flight

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