Abstract
Image segmentation is an activity of dividing an image into multiple segments. Thresholding is a typical step for analyzing image, recognizing the pattern, and computer vision. Threshold value can be calculated using histogram as well as using Gaussian mixture model. but those threshold values are not the exact solution to do the image segmentation. To overcome this problem and to find the exact threshold value, differential evolution algorithm is applied. Differential evolution is considered to be meta-heuristic search and useful in solving optimization problems. DE algorithms can be applied to process Image Segmentation by viewing it as an optimization problem. In this paper, Different Differential evolution (DE) algorithms are used to perform the image segmentation and their performance is compared in solving image segmentation. Both 2 class and 3-class segmentation is applied and the algorithm performance is analyzed. Experimental results shows that DE/best/1/bin algorithm out performs than the other variants of DE algorithms
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Kaur, H., Sohi, N.: A study for applications of histogram in image enhancement. Int. J. Eng. Sci. 6, 59–63 (2017)
Kotte, S., Kumar, P.R., Injeti, S.K.: An efficient approach for optimal multilevel thresholding selection for gray scale images based on improved differential search algorithm (2016)
Chen, B., Zeng, W., Lin, Y., Zhong, Q.: An enhanced differential evolution based algorithm with simulated annealing for solving multiobjective optimization problems (2014)
Farnoosh, R., Yari, G., Zarpak, B.: Image segmentation using Gaussian mixture model. Int. J. Eng. Sci. 19, 29–32 (2008)
Tang, L., Dong, Y., Liu, J.: Differential evolution with an Individual-dependent mechanism. IEEE Trans. Evol. Comput. 19(4), 560–574 (2015)
Huang, Z., Chen, Y.: An improved differential evolution algorithm based on adaptive parameter. J. Control Sci. Eng. 2013, 5 (2013). Article ID 462706
Cuevas, E., Zaldívar, D., Perez-Cisneros, M.A.: Image Segmentation Based on Differential Evolution Optimization, pp. 9–21. Springer International Publishing, Switzerland (2016)
Tvrdik, J.: Adaptive differential evolution and exponential crossover. IEEE (2008)
Weber, M., Neri, F.: Contiguous Binomial Crossover in Differential Evolution. Springer-Verlag, Heidelberg (2012)
Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution–an updated survey. Swarm Evol. Comput. 27, 1–30 (2016)
Haritha, K.C., Thangavelu, S.: Multi-focus region-based image fusion using differential evolution algorithm variants. In: Computational Vision and Biomechanics. LNCS, vol. 28, pp.579–592. Springer, Netherlands (2018)
Suganya, M., Menaka, M.: Various segmentation techniques in image processing: a survey. Int. J. Innov. Res. Comput. Commun. Eng. 2(1), 1048–1052 (2014)
Kaur, A., Kaur, N.: Image segmentation techniques. Int. Res. J. Eng. Technol. 02(02), 944–947 (2015)
Zaitouna, N.M., Aqelb, M.J.: Survey on image segmentation techniques. In: International Conference on Communication Management and Information Technology. Elsevier (2015)
Choudhary, R., Gupta, R.: Recent trends and techniques in image enhancement using DE – a survey. Int. J. Adv. Res. Comput. Sci. 7(4), 106–112 (2017)
Kaur, B., Kaur, P.: A comparitive study of image segmentation techniques. Int. J. Comput. Sci. Eng. 3(12), 50–56 (2015)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.: Image thresholding using differential evolution. In: Proceedings of International Conference on Image Processing, Computer Vision and Pattern Recognition, Las Vegas, USA, pp. 244–249 (2006)
Osuna-Enciso, V., Cuevas, E., Sossa, H.: A Comparison of nature inspired algorithms for multi-thresholding image segmentation. Expert Syst. Appl. 40(4), 1213–1219 (2013)
Ochoa-Monitel, R., Carrasco Aguliar, M.A., Sanchez-Lopez, C.: Image segmentation by using differential evolution with constraints handling. IEEE (2017)
Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)
Ali, M., Siarry, P., Pant, M.: Multi-level image thresholding based on hybrid DE algorithm. Application of medical images. Springer-Verlag Gmbh, Germany (2017)
Leon, M., Xiong, N.: Investigation of mutation strategies in differential evolution for solving global optimization problems, vol. 8467, pp. 372–383. Springer International Publishing, Switzerland (2014)
Thangavelu, S., ShanmugaVelayutham, C.: An investigation on mixing heterogeneous differential evolution variants in a distributed framework. Int. J. Bio-Inspired Comput. 7(5), 307–320 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
✓ All authors declare that there is no conflict of interest.
✓ No humans/animals involved in this research work.
✓ We have used our own data.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
SandhyaSree, V., Thangavelu, S. (2020). Performance Analysis of Differential Evolution Algorithm Variants in Solving Image Segmentation. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-37218-7_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37217-0
Online ISBN: 978-3-030-37218-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)