Skip to main content
SpringerLink
Log in

An improved cuckoo search algorithm for multi-level gray-scale image thresholding

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In decades, Yang’s cuckoo search algorithm has been widely developed to select the optimal threshold of bi-level image threshoding, but the amount of computation of which increases exponentially with multi-level thresholding. To reduce the computation quantity, the iterative step size is adaptively decided by its fitness values of the current iteration without using the Lévy distribution in this study. The modification may cause the solution drops into the local optima during the later period. Therefore, the constant discovery probability pa is automatically changed relating to the current and total iterations. And then, to verify segmentation accuracy and efficiency of the proposed method, an adaptive cuckoo search algorithm proposed by Naik and Yang’s cuckoo search algorithm are included to test on several gray-scale images. The results show that the proposed algorithm is expert in selecting optimal thresholds for segmenting gray-scale image.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Abhinaya B, Sri Madhava Raja N (2015) Solving Multi-level Image Thresholding Problem-An Analysis with Cuckoo Search Algorithm. Adv Intell Syst Comput 339:177–186

    Google Scholar 

  2. Agrawal S, Panda R, Bhuyan S, Panigrahi BK (2013) Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm. Swarm Evol Comput 11:16–30

    Article  Google Scholar 

  3. Alihodzic A, Tuba M (2014) Improved bat algorithm applied to multilevel image thresholding. Sci World J 2014:1–16

    Article  Google Scholar 

  4. Bhandari AK, Kumar A, Singh GK (2015) Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 42(3):1573–1601

    Article  Google Scholar 

  5. Bhandari AK, Singh VK, Singh GK, Singh GK (2014) Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Syst Appl 41(7):3538–3560

    Article  Google Scholar 

  6. Feng YC, Shen XJ, Chen HP, Zhang XL (2017) Segmentation fusion based on neighboring information for MR brain images. Multi Tools Appli 76(22):23139–23161

    Article  Google Scholar 

  7. Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira NMF (2012) An efficient method for segmentation of images based on fractional calculus and natural selection. Expert Syst Appl 39(16):12407–12417

    Article  Google Scholar 

  8. Hammouche K, Diaf M, Siarry P (2008) A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation. Comput Vision Image Understan 109(2):163–175

    Article  Google Scholar 

  9. Kapur JN, Sahoo PK, Wong AKC (1985) A new method for gray-level picture thresholding using the entropy of the histogram. Comput Vision Graphics Image Process 29(3):273–285

    Article  Google Scholar 

  10. Li XT, Yin MH (2015) Modified cuckoo search algorithm with self adaptive parameter method. Inf Sci 298(20):80–97

    Article  Google Scholar 

  11. Mantegna RN (1994) Fast, accurate algorithm for numerical simulation of lévy stable stochastic processes. Phys Rev E 49(5):4677–4683

    Article  Google Scholar 

  12. Naik MK, Nath MR, Wunnava A, Sahany S, Panda R (2015) A new adaptive Cuckoo search algorithm. In: Proceeding of international conference on recent trends in information systems, pp 1–5

  13. Naik MK, Panda R (2016) A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition. Appl Soft Comput 38:661–675

    Article  Google Scholar 

  14. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  15. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294

    Article  Google Scholar 

  16. Panda R, Agrawal S, Bhuyan S (2013) Edge magnitude based multilevel thresholding using Cuckoo search technique. Expert Syst Appl 40(18):7617–7628

    Article  Google Scholar 

  17. Portes de Albuquerque M, Esquef IA, Gesualdi Mello AR (2004) Image thresholding using Tsallis entropy. Pattern Recogn Lett 25(9):1059–1065

    Article  Google Scholar 

  18. Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Comput Vis Graph Image Process 41(2):233–260

    Article  Google Scholar 

  19. Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–168

    Article  Google Scholar 

  20. Sharma A, Chaturvedi R, Dwivedi U, Kumar S, Reddy S (2018) Firefly algorithm based Effective gray scale image segmentation using multilevel thresholding and Entropy function. Int J Pure Appl Math 118(5):437–443

    Google Scholar 

  21. Suresh S, Lal S (2016) An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions. Expert Syst Appl 58:184–209

    Article  Google Scholar 

  22. Tiwari V (2012) Face recognition based on cuckoo search algorithm. Ind J Comput Sci Eng 3(3):401–405

    Google Scholar 

  23. Tsallis C (1988) Possible generalization of Boltzmann-Gibbs statistics. J Stat Phys 52(1):479–487

    Article  MathSciNet  MATH  Google Scholar 

  24. Valian E, Mohanna S, Tavakoli S (2011) Improved cuckoo search algorithm for feed forward neural network training. Int J Artif Intell Appl 2(3):36–43

    Google Scholar 

  25. Wang LJ, Zhong YW (2015) Cuckoo search algorithm with chaotic maps. Math Probl Eng 2015:1–14

    MathSciNet  MATH  Google Scholar 

  26. Wang W, Xie C (2018) A cuckoo search algorithm based on self-adjustment strategy. J Phys Conference Series 1087(2):1–7

    Google Scholar 

  27. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13 (4):600–612

    Article  Google Scholar 

  28. Wei HT, Yang Q (2017) A multilevel threshold segmentation technique using self-adaptive Cuckoo search algorithm. In: Advanced Information Technology, Electronic and Automation Control Conference, pp 2292–2295

  29. Yang XS, Deb S (2010) Engineering optimisation by cuckoo search. Int J Math Model Numer Optim 1(4):330–343

    MATH  Google Scholar 

  30. Yang XS, Deb S (2013) Multiobjective cuckoo search for design optimization. Comput Oper Res 40(6):1616–1624

    Article  MathSciNet  MATH  Google Scholar 

  31. Yang XS, Suash D (2009) Cuckoo search via lévy flights, NaBIC, USA

  32. Zhang YD, Wu LN (2011) Optimal Multi-Level thresholding based on maximum tsallis entropy via an artificial bee colony approach. Entropy 13(4):841–859

    Article  MathSciNet  MATH  Google Scholar 

  33. Zhou YQ, Yang X, Ling Y, Zhang JZ (2018) Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77 (18):23699–23727

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No.11601007 and No.11701007.

Author information

Authors and Affiliations

  1. School of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, Anhui, 232001, China

    Min Sun & Hui Wei

Authors
  1. Min Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Wei.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, M., Wei, H. An improved cuckoo search algorithm for multi-level gray-scale image thresholding. Multimed Tools Appl 79, 34993–35016 (2020). https://doi.org/10.1007/s11042-020-08931-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-08931-5

Keywords

Search

Navigation