Abstract
Multilevel thresholding based on Otsu method is one of the most popular image segmentation techniques. However, when the number of thresholds increases, the consumption of CPU time grows exponentially. Although the evolution algorithms are helpful to solve this problem, for the high-dimensional problems, the Otsu methods based on the classical evolution algorithms may get trapped into local optimal or be instability due to the inefficiency of local search. To overcome such drawback, this paper employs the self-adaptive multiple evolution algorithms (MEAs), which automatically protrudes the core position of the excellent algorithm among the selected algorithms. The tests against 10 benchmark functions demonstrate that this multi-algorithms is fit for most problems. Then, this optimizer is applied to image multilevel segmentation problems. Experimental results on a variety of images provided by the Berkeley Segmentation Database show that the proposed algorithm can accurately and stably solve this kind of problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bexdek, J.C.: A convergence theorem for the fuzzy isodara clustering algorithms. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–2(1), 1–8 (1980)
Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: Proceedings of 7th International Conference on Computer Vision, pp. 1197–1203, Kerkyra (1999)
Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990)
Agrawal, S., Panda, R., Bhuyan, S., Panigrahi, B.K.: Tsallis: entropy based optimal multi-level thresholding using cuckoo search algorithm. Swarm Evol. Comput. 11, 16–30 (2013)
Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)
Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. Comput. Vis. Graph Image Process. 41(2), 233–260 (1988)
Gao, H., Xu, W., Sun, J., Tang, Y.: Multilevel thresholding for image segmentation through an improved quantum-behaved particle swarm algorithm. IEEE T. Instrum. Measur. 59(4), 290–301 (2010)
Vrugt, J.A., Robinson, B.A., Hyman, J.M.: Self-adaptive multimethod search for global optimization in real-parameter spaces. IEEE Trans. Evol. Comput. 13(2), 243–259 (2009)
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Kennedy, J.: The particle swarm as collaborative sampling of the search space. Adv. Complex Syst. 10, 191–213 (2007)
Li, W., Zhou, Q., Zhu, Y., Pan, F.: An improved MOPSO with a crowding distance based external archive maintenance strategy. In: Tan, Y., Shi, Y., Ji, Z. (eds.) ICSI 2012, Part I. LNCS, vol. 7331, pp. 74–82. Springer, Heidelberg (2012)
Chen, H.N., Zhu, Y.L., Hu, K.Y., Ku, T.: RFID network planning using a multi-swarm optimizer. J. Netw. Comput. Appl. 34(3), 88–901 (2011)
El-Abd, M.: Performance assessment of foraging algorithms vs. evolutionary algorithms. Inf. Sci. 182(1), 243–263 (2012)
Kmpf, J.H., Robinson, D.: A hybrid CMA-ES and HDE optimization algorithm with application to solar energy potential. Appl. Soft Comput. 9(2), 738–745 (2009)
Civicioglu, P., Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 39(4), 315–346 (2013)
Simon, D.: Biogeography-based optimization. IEEE Trans. Evol. Comput. 12(6), 702–713 (2008)
Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y.P., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical report, Nanyang Technological University, Singapore and Kan-GAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur), pp. 1–50 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, L., Hu, J., Lin, N., Zhang, Q., Chen, H. (2015). Self-adaptive Multiple Evolution Algorithms for Image Segmentation Using Multilevel Thresholding. In: Gong, M., Linqiang, P., Tao, S., Tang, K., Zhang, X. (eds) Bio-Inspired Computing -- Theories and Applications. BIC-TA 2015. Communications in Computer and Information Science, vol 562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49014-3_36
Download citation
DOI: https://doi.org/10.1007/978-3-662-49014-3_36
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49013-6
Online ISBN: 978-3-662-49014-3
eBook Packages: Computer ScienceComputer Science (R0)