Cuckoo Search and Firefly Algorithm Applied to Multilevel Image Thresholding

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 516)

Abstract

Multilevel image thresholding is a technique widely used in image processing, most often for segmentation. Exhaustive search is computationally prohibitively expensive since the number of possible thresholds to be examined grows exponentially with the number of desirable thresholds. Swarm intelligence metaheuristics have been used successfully for such hard optimization problems. In this chapter we investigate performance of two relatively new swarm intelligence algorithms, cuckoo search and firefly algorithm, applied to multilevel image thresholding. Particle swarm optimization and differential evolution algorithms have also been implemented for comparison. Two different objective functions, Kapur’s maximum entropy thresholding function and multi Otsu between-class variance, were used on standard benchmark images with known optima from exhaustive search (up to five threshold points). Results show that both, cuckoo search and firefly algorithm, exhibit superior performance and robustness.

Keywords

Swarm intelligence Nature inspired algorithms Optimization metaheuristics Cuckoo search Firefly algorithm Image processing Multilevel image thresholding 

Notes

Acknowledgments

This reserach was supported by Ministry of Education and Science of Republic of Serbia, Grant III-44006.

References

  1. 1.
    Adollah, R., Mashor, M.Y., Rosline, H., Harun, N.H.: Multilevel thresholding as a simple segmentation technique in acute leukemia images. J. Med. Imaging Health Inf. 2(3), 285–288 (2012)CrossRefGoogle Scholar
  2. 2.
    Akay, B.: A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl. Soft Comput. 13(6), 3066–3091 (2013)CrossRefGoogle Scholar
  3. 3.
    Bacanin, N., Tuba, M.: Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators. Stud. Inf. Control 21(2), 137–146 (2012)Google Scholar
  4. 4.
    Brajevic, I., Tuba, M.: An upgraded artificial bee colony algorithm (ABC) for constrained optimization problems. J. Intell. Manuf. 24(4), 729– 740 (2013)Google Scholar
  5. 5.
    Dai, C., Chen, W., Song, Y., Zhu, Y.: Seeker optimization algorithm: a novel stochastic search algorithm for global numerical optimization. J. Syst. Eng. Electron. 21(2), 300–311 (2010)Google Scholar
  6. 6.
    Dominguez, A.R., Nandi, A.K.: Detection of masses in mammograms via statistically based enhancement, multilevel-thresholding segmentation, and region selection. Comput. Med. Imaging Graph. 32(4), 304–315 (2008)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., Gambardella, L.M.: Ant colonies for the travelling salesman problem. Biosystems 43(2), 73–81 (1997)CrossRefGoogle Scholar
  8. 8.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Mixed variable structural optimization using firefly algorithm. Comput. Struct. 89(23–24), 2325–2336 (2011)CrossRefGoogle Scholar
  9. 9.
    Gandomi, A.H., Yang, X.S.: Evolutionary boundary constraint handling scheme. Neural Comput. Appl. 21(6, SI), 1449–1462 (2012)CrossRefGoogle Scholar
  10. 10.
    Gandomi, A.H., Yang, X.S., Alavi, A.H.: Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems. Eng. Comput. 29(1), 17–35 (2013)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Gandomi, A.H., Yang, X.S., Talatahari, S., Alavi, A.H.: Firefly algorithm with chaos. Commun. Nonlinear Sci. Numer. Simul. 18(1), 89–98 (2013)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Hammouche, K., Diaf, M., Siarry, P.: A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem. Eng. Appl. Artif. Intell. 23(5), 676–688 (2010)CrossRefGoogle Scholar
  13. 13.
    Harrabi, R., Ben Braiek, E.: Color image segmentation using multi-level thresholding approach and data fusion techniques: application in the breast cancer cells images. EURASIP J. Image Video Process. (2012)Google Scholar
  14. 14.
    Heikkonen, J., Mantynen, N.: A computer vision approach to digit recognition on pulp bales. Pattern Recogn. Lett. 17(4), 413–419 (1996) (International Conference on Engineering Applications of Neural Networks (EANN 95), Otaniemi, Finland, 21–23 August 1995)Google Scholar
  15. 15.
    Horng, M.H.: Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization. Expert Syst. Appl. 37(6), 4580–4592 (2010)CrossRefGoogle Scholar
  16. 16.
    Horng, M.H.: Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Syst. Appl. 38(11), 13,785–13,791 (2011)Google Scholar
  17. 17.
    Horng, M.H.: Vector quantization using the firefly algorithm for image compression. Expert Syst. Appl. 39(1), 1078–1091 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Jaynes, E.T.: Information theory and statistical mechanics. Phys. Rev. Ser. II 106(4), 620–630 (1957)MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Jovanovic, R., Tuba, M.: An ant colony optimization algorithm with improved pheromone correction strategy for the minimum weight vertex cover problem. Appl. Soft Comput. 11(8), 5360–5366 (2011)CrossRefGoogle Scholar
  20. 20.
    Jovanovic, R., Tuba, M.: Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem. Comput. Sci. Inf. Syst. (ComSIS) 10(1), 133–149 (2013)CrossRefGoogle Scholar
  21. 21.
    Kapur, E.J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray-level picture thresholding using the entropy of the histogram. Comput. Vis. Graphics Image Process. 29(3), 273–285 (1985)CrossRefGoogle Scholar
  22. 22.
    Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Engineering Faculty, Computer Engineering Department, Erciyes University (2005)Google Scholar
  23. 23.
    Kazem, A., Sharifi, E., Hussain, F.K., Saberi, M., Hussain, O.K.: Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13(2), 947–958 (2013)CrossRefGoogle Scholar
  24. 24.
    Marichelvam, M.K.: An improved hybrid Cuckoo Search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems. Int. J. Bio-Inspired Comput. 4(4, SI), 200–205 (2012)CrossRefGoogle Scholar
  25. 25.
    Ng, H.F.: Automatic thresholding for defect detection. Pattern Recogn. Lett. 27(14), 1644–1649 (2006)CrossRefGoogle Scholar
  26. 26.
    Otsu, N.: A threshold selection method for grey level histograms. EEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Portes de Albuquerque, M., Esquef, I.A., Gesualdi Mello, A.R.: Image thresholding using tsallis entropy. Pattern Recogn. Lett. 25(9), 1059–1065 (2004)Google Scholar
  28. 28.
    Sahoo, P., Wilkins, C., Yeager, J.: Threshold selection using Renyi’s entropy. Pattern Recogn. 30(1), 71–84 (1997)CrossRefMATHGoogle Scholar
  29. 29.
    Sarkar, S., Patra, G.R., Das, S.: A differential evolution based approach for multilevel image segmentation using minimum cross entropy thresholding. In: Proceedings of the 2nd International Conference on Swarm, Evolutionary, and Memetic Computing, Part I, pp. 51–58 (2011)Google Scholar
  30. 30.
    Sathya, P.D., Kayalvizhi, R.: Optimal multilevel thresholding using bacterial foraging algorithm. Expert Syst. Appl. 38(12), 15,549–15,564 (2011)CrossRefGoogle Scholar
  31. 31.
    Sathya, P.D., Kayalvizhi, R.: Modified bacterial foraging algorithm based multilevel thresholding for image segmentation. Eng. Appl. Artif. Intell. 24(4), 595–615 (2011)CrossRefGoogle Scholar
  32. 32.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electron. Imaging 13(1), 146–165 (2004)CrossRefGoogle Scholar
  33. 33.
    Srivastava, P.R., Varshney, A., Nama, P., Yang, X.S.: Software test effort estimation: a model based on cuckoo search. Int. J. Bio-Inspired Comput. 4(5), 278–285 (2012)CrossRefGoogle Scholar
  34. 34.
    Tuba, M., Subotic, M., Stanarevic, N.: Performance of a modified cuckoo search algorithm for unconstrained optimization problems. WSEAS Trans. Syst. 11(2), 62–74 (2012)Google Scholar
  35. 35.
    Tuba, M., Jovanovic, R.: Improved ant colony optimization algorithm with pheromone correction strategy for the traveling salesman problem. Int. J. Comput. Commun. Control 8(3), 477–485 (2013)MathSciNetGoogle Scholar
  36. 36.
    Tuba, M., Brajevic, I., Jovanovic, R.: Hybrid seeker optimization algorithm for global optimization. Appl. Math. Inf. Sci. 7(3), 867–875 (2013)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Tuba, M.: Asymptotic behavior of the maximum entropy routing in computer networks. Entropy 15(1), 361–371 (2013)MathSciNetCrossRefGoogle Scholar
  38. 38.
    Yan, H.: Unified formulation of a class of optimal image thresholding techniques. Pattern Recogn. 29(12), 2025–2032 (1996)CrossRefGoogle Scholar
  39. 39.
    Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of the World Congress on Nature & Biologically Inspired, Computing, pp. 210–214 (2009)Google Scholar
  40. 40.
    Yang, X.S.: Firefly algorithm, Lévy flights and global optimization. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems, vol. XXVI, pp. 209–218. Springer, London (2010)Google Scholar
  41. 41.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds) Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, pp. 169–178. Springer, Berlin (2009)Google Scholar
  42. 42.
    Yang, X.S.: Free lunch or no free lunch: that is not just a question? Int. J. Artif. Intell. Tools 21(3, SI) (2012)Google Scholar
  43. 43.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, Frome (2010)Google Scholar
  44. 44.
    Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optimisation 1(4), 330–343 (2010)CrossRefMATHGoogle Scholar
  45. 45.
    Yang, X.S.: Firefly algorithm, stochastic test functions and design optimisation. Int. J. Bio-Inspired Comput. 2(2), 78–84 (2010)CrossRefGoogle Scholar
  46. 46.
    Yang, X.S.: Review of meta-heuristics and generalised evolutionary walk algorithm. Int. J. Bio-Inspired Comput. 3(2), 77–84 (2011)CrossRefGoogle Scholar
  47. 47.
    Yang, X.S.: Efficiency analysis of swarm intelligence and randomization techniques. J. Comput. Theor. Nanosci. 9(2), 189–198 (2012)CrossRefGoogle Scholar
  48. 48.
    Yang, X.S., Hosseini, S.S.S., Gandomi, A.H.: Firefly algorithm for solving non-convex economic dispatch problems with valve loading effect. Appl. Soft Comput. 12(3), 1180–1186 (2012)CrossRefGoogle Scholar
  49. 49.
    Yang, X.S.: Multiobjective firefly algorithm for continuous optimization. Eng. Comput. 29(2), 175–184 (2013)CrossRefGoogle Scholar
  50. 50.
    Yen, J.C., Chang, F.J., Chang, S.: A new criterion for automatic multilevel thresholding. IEEE Trans. Image Process. 4(3), 370–378 (1995)CrossRefGoogle Scholar
  51. 51.
    Yin, P.Y.: Multilevel minimum cross entropy threshold selection based on particle swarm optimization. Appl. Math. Comput. 184(2), 503–513 (2007)MathSciNetCrossRefMATHGoogle Scholar
  52. 52.
    Zhou, Y., Zheng, H., Luo, Q., Wu, J.: An improved Cuckoo search algorithm for solving planar graph coloring problem. Appl. Math. Inf. Sci. 7(2), 785–792 (2013)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of BelgradeBelgradeSerbia
  2. 2.Megatrend University BelgradeN. BelgradeSerbia

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