Image Segmentation Using Artificial Bee Colony Optimization

  • Erik Cuevas
  • Felipe Sención-Echauri
  • Daniel Zaldivar
  • Marco Pérez
Part of the Intelligent Systems Reference Library book series (ISRL, volume 38)


This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pixel classification for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation-Maximization (EM) algorithm, the ABC-based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithm’s ability to perform automatic multi-threshold selection yet showing interesting advantages by comparison to other well-known algorithms.


Image Segmentation Expectation Maximization Gaussian Mixture Model Swarm Intelligence Expectation Maximization Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Abak, T., Baris, U., Sankur, B.: The performance of thresholding algorithms for optical character recognition. In: Proceedings of International Conference on Document Analytical Recognition, pp. 697–700 (1997)Google Scholar
  2. 2.
    Kamel, M., Zhao, A.: Extraction ofbinary character/graphics images from grayscale document images. Graph. Models Image Process. 55(3), 203–217 (1993)CrossRefGoogle Scholar
  3. 3.
    Trier, O.D., Jain, A.K.: Goal-directed evaluation ofbinarization methods. IEEE Trans. Pattern Anal. Mach. Intell. 17(12), 1191–1201 (1995)CrossRefGoogle Scholar
  4. 4.
    Bhanu, B.: Automatic target recognition: state ofthe art survey. IEEE Trans. Aerosp. Electron. Syst. 22, 364–379 (1986)CrossRefGoogle Scholar
  5. 5.
    Sezgin, M., Sankur, B.: Comparison ofthresholding methods for non-destructive testing applications. In: IEEE International Conference on Image Processing, pp. 764–767 (2001)Google Scholar
  6. 6.
    Sezgin, M., Tasaltin, R.: A new dichotomization technique to multilevel thresholding devoted to inspection applications. Pattern Recognition Lett. 21(2), 151–161 (2000)CrossRefGoogle Scholar
  7. 7.
    Guo, R., Pandit, S.M.: Automatic threshold selection based on histogram modes and discriminant criterion. Mach. Vis. Appl. 10, 331–338 (1998)CrossRefGoogle Scholar
  8. 8.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognit. 26, 1277–1294 (1993)CrossRefGoogle Scholar
  9. 9.
    Shaoo, P.K., Soltani, S., Wong, A.K.C., Chen, Y.C.: Survey: A survey of thresholding techniques. Comput. Vis. Graph. Image Process. 41, 233–260 (1988)CrossRefGoogle Scholar
  10. 10.
    Snyder, W., Bilbro, G., Logenthiran, A., Rajala, S.: Optimal thresholding: A new approach. Pattern Recognit. Lett. 11, 803–810 (1990)zbMATHCrossRefGoogle Scholar
  11. 11.
    Chen, S., Wang, M.: Seeking multi-thresholds directly from support vectors for image segmentation. Neurocomputing 67(4), 335–344 (2005)CrossRefGoogle Scholar
  12. 12.
    Chih-Chih, L.: A Novel Image Segmentation Approach Based on Particle Swarm Optimization. IEICE Trans. Fundamentals 89(1), 324–327 (2006)Google Scholar
  13. 13.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Addison Wesley, Reading (1992)Google Scholar
  14. 14.
    Gupta, L., Sortrakul, T.: A Gaussian-Mixture-Based Image segmentation Algorithm. Pattern Recognition 31(3), 315–325 (1998)CrossRefGoogle Scholar
  15. 15.
    Dempster, A.P., Laird, A.P., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. Ser. B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Zhang, Z., Chen, C., Sun, J., Chan, L.: EM algorithms for Gaussian mixtures with split-and-merge operation. Pattern Recognition 36, 1973–1983 (2003)zbMATHCrossRefGoogle Scholar
  17. 17.
    Park, H., Amari, S., Fukumizu, K.: Adaptive natural gradient learning algorithms for various stochastic models. Neural Networks 13, 755–764 (2000)CrossRefGoogle Scholar
  18. 18.
    Park, H., Ozeki, T.: Singularity and slow Convergence of the EM algorithm for Gaussian Mixtures. Neural Process Lett. 29, 45–59 (2009)CrossRefGoogle Scholar
  19. 19.
    Cuevas, E., Zaldivar, D., Perez-Cisneros, M.: Seeking multi-thresholds for image segmentation with Learning Automata. In: Machine Vision and Applications (2010), doi:10.1007/s00138-010-0249-0Google Scholar
  20. 20.
    Cuevas, E., Zaldivar, D., Perez-Cisneros, M.: A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Systems with Applications 37(7), 5265–5271 (2010)CrossRefGoogle Scholar
  21. 21.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization, Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)Google Scholar
  22. 22.
    Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)CrossRefGoogle Scholar
  23. 23.
    Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Applied Mathematics and Computation 214, 108–132 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Karaboga, N.: A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of the Franklin Institute 346, 328–348 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  25. 25.
    Pan, Q.-K., Fatih Tasgetiren, M., Suganthan, P.N., Chua, T.J.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences, doi:10.1016/j.ins.2009.12.025Google Scholar
  26. 26.
    Kang, F., Li, J., Xu, Q.: Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers and Structures 87, 861–870 (2009)CrossRefGoogle Scholar
  27. 27.
    Zhang, C., Ouyang, D., Ning, J.: An artificial bee colony approach for clustering. Expert Systems with Applications 37, 4761–4767 (2010)CrossRefGoogle Scholar
  28. 28.
    Karaboga, D., Ozturk, C.: A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 11, 652–657 (2011)CrossRefGoogle Scholar
  29. 29.
    Ho, S.L., Yang, S.: An artificial bee colony algorithm for inverse problems. International Journal of Applied Electromagnetics and Mechanics 31, 181–192 (2009)Google Scholar
  30. 30.
    Floudas, C., Akrotirianakis, I., Caratzoulas, S., Meyer, C., Kallrath, J.: Global optimization in the 21st century: Advances and challenges. Computers & Chemical Engineering 29(6), 1185–1202 (2005)CrossRefGoogle Scholar
  31. 31.
    Georgieva, A., Jordanov, I.: Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms. European Journal of Operational Research 196, 413–422 (2009)zbMATHCrossRefGoogle Scholar
  32. 32.
    Blake, A.: Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence 11, 2–12 (1989)zbMATHCrossRefGoogle Scholar
  33. 33.
    Decker, A., Aarts, E.: Global optimization and simulated annealing. Mathematical Programming 50, 367–393 (1991)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  35. 35.
    Bai, H., Zhao, B.: A Survey on Application of Swarm Intelligence Computation to Electric Power System. In: Intelligent Control and Automation, WCICA 2006, pp. 7587–7591 (2006)Google Scholar
  36. 36.
    Tsetlin, M.L.: Automaton Theory and Modeling of Biological Systems. Academic Press, London (1973)Google Scholar
  37. 37.
    Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31, 61–85 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Erik Cuevas
    • 1
  • Felipe Sención-Echauri
    • 1
  • Daniel Zaldivar
    • 1
  • Marco Pérez
    • 1
  1. 1.Departamento de Ciencias ComputacionalesUniversidad de Guadalajara, CUCEIGuadalajaraMéxico

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