Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

A Correction to this article was published on 29 November 2021

This article has been updated

Abstract

Multilevel thresholding image segmentation has always been a popular issue and attracted the attention of many researchers in recent years since it has many applications. However, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To address this problem, in this paper, the optimal thresholds for multilevel thresholding image segmentation are obtained by maximizing the Rényi entropy using the Lévy flight firefly algorithm (FAFA), in which an adaptive parameter strategy based on Lévy flight is used to improve the performance of the standard firefly algorithm (FA). The performance of the proposed algorithm has been evaluated using several benchmark images and has been compared with five different heuristic algorithms. The experimental results showed that the proposed algorithm prevailed over the other five competitive algorithms in terms of the objective function value, image quality measures, and computational efficiency.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Change history

References

  1. Hesamian MH, Jia W, He X et al (2019) Deep learning techniques for medical image segmentation: achievements and challenges. J Digit Imaging 32(4):582–596

    Article  Google Scholar 

  2. Dhal KG, Das A, Ray S et al (2020) Nature-inspired optimization algorithms and their application in multi-thresholding image segmentation. Arch Comput Methods Eng 27(3):855–888

    Article  MathSciNet  Google Scholar 

  3. Li K, Tan Z (2019) An improved flower pollination optimizer algorithm for multilevel image thresholding. IEEE access 7:165571–165582

    Article  Google Scholar 

  4. Pare S, Kumar A, Singh G K, et al (2020) Image segmentation using multilevel thresholding: a research review. Iranian J Sci Technol Trans Electrical Eng, 1–29

  5. Abd Elaziz M, Ewees A A, Oliva D (2020) Hyper-heuristic method for multilevel thresholding image segmentation. Expert Syst Appl 146: 113201

  6. Goh TY, Basah SN, Yazid H et al (2018) Performance analysis of image thresholding: Otsu technique. Measurement 114:298–307

    Article  Google Scholar 

  7. Upadhyay P, Chhabra J K (2020) Kapur’s entropy based optimal multilevel image segmentation using crow search algorithm. Appl Soft Comput 97:105522

  8. Raja N S M, Fernandes S L, Dey N, et al (2018) Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation. Jf Ambient Intell Humanized Comput, 1–12

  9. Li J, Tang W, Wang J et al (2019) A multilevel color image thresholding scheme based on minimum cross entropy and alternating direction method of multipliers. Optik 183:30–37

    Article  Google Scholar 

  10. Yimit A, Hagihara Y (2018) 2D direction histogram-based Rényi entropic multilevel thresholding. J Adv Comput Intell Intelligent Informatics 22(3):369–379

    Article  Google Scholar 

  11. Al-Janabi S, Alkaim AF, Adel Z (2020) An Innovative synthesis of deep learning techniques (DCapsNet & DCOM) for generation electrical renewable energy from wind energy. Soft Comput 24(14):10943–10962

    Article  Google Scholar 

  12. Tan Z, Li K (2021) Differential evolution with mixed mutation strategy based on deep reinforcement learning. Appl Soft Comput 111: 107678.

  13. Gao H, Fu Z, Pun CM et al (2018) A multi-level thresholding image segmentation based on an improved artificial bee colony algorithm. Comput Electr Eng 70:931–938

    Article  Google Scholar 

  14. Mishra S, Panda M (2018) Bat algorithm for multilevel colour image segmentation using entropy-based thresholding. Arab J Sci Eng 43(12):7285–7314

    Article  Google Scholar 

  15. Sharma A, Chaturvedi R, Dwivedi UK et al (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 

  16. 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 

  17. Erwin S, Saputri W (2018) Hybrid multilevel thresholding and improved harmony search algorithm for segmentation. Int J Electrical Comp Eng (IJECE) 8(6):4593–4602

    Article  Google Scholar 

  18. Farshi TR, Orujpour M (2019) Multi-level image thresholding based on social spider algorithm for global optimization. Int J Inf Technol 11(4):713–718

    Google Scholar 

  19. Shen L, Fan C, Huang X (2018) Multi-level image thresholding using modified flower pollination algorithm. IEEE Access 6:30508–30519

    Article  Google Scholar 

  20. Lang C, Jia H (2019) Kapur’s entropy for color image segmentation based on a hybrid whale optimization algorithm. Entropy 21(3):318

    Article  MathSciNet  Google Scholar 

  21. Khairuzzaman AKM, Chaudhury S (2020) Modified Moth-Flame Optimization Algorithm-Based Multilevel Minimum Cross Entropy Thresholding for Image Segmentation. Int J Swarm Intell Res (IJSIR) 11(4):123–139

    Article  Google Scholar 

  22. Tan Z, Zhang D (2020) A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation. J Ambient Intell Humanized Comput, pp 1–12

  23. Tan Z, Li K, Wang Y (2021) Differential evolution with adaptive mutation strategy based on fitness landscape analysis. Inf Sci 549:142–163

    Article  MathSciNet  Google Scholar 

  24. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1(1):36–50

    Article  Google Scholar 

  25. Cui Z, Sun B, Wang G et al (2017) A novel oriented cuckoo search algorithm to improve DV-Hop performance for cyber–physical systems. J Parallel Distributed Comput 103:42–52

    Article  Google Scholar 

  26. Zhang Y, Song X, Gong D (2017) A return-cost-based binary firefly algorithm for feature selection. Inf Sci 418:561–574

    Article  Google Scholar 

  27. Raja N S, Manic K S, Rajinikanth V et al (2013) Firefly Algorithm with Various Randomization Parameters: An Analysis. Swarm evolutionary and memetic computing, 110–121.

  28. Fister I, Perc M, Kamal S M, et al (2015) A review of chaos-based firefly algorithms. Appl Math Comput, 155–165

  29. Rajinikanth V, Couceiro MS (2015) RGB histogram based color image segmentation using firefly algorithm. Proc Comput Sci 46:1449–1457

    Article  Google Scholar 

  30. Wang H, Wang W, Cui Z et al (2018) A new dynamic firefly algorithm for demand estimation of water resources. Inf Sci 438:95–106

    Article  MathSciNet  Google Scholar 

  31. Yu S, Zhu S, Ma Y, et al (2015) A variable step size firefly algorithm for numerical optimization. Appl Mathe Computn, 214–220.

  32. Verma O P, Aggarwal D, Patodi T et al (2016) Opposition and dimensional based modified firefly algorithm. Expert Syst Appl, 168–176

  33. Haklı H, Uğuz H (2014) A novel particle swarm optimization algorithm with Levy flight. Appl Soft Comput 23:333–345

    Article  Google Scholar 

  34. Lin B, Huang Y, Zhang J, Junqin Hu, Xing Chen, Jun Li (2020) Cost-Driven offloading for DNN-based applications over Cloud, edge and end devices. IEEE Trans Industr Inf 16(8):5456–5466

  35. Khairuzzaman AKM, Chaudhury S (2017) Multilevel thresholding using grey wolf optimizer for image segmentation. Expert Syst Appl 86:64–76

    Article  Google Scholar 

  36. Huang G, Liu X, Ma Y, Xuan Lu, Zhang Y, Xiong Y (2019) Programming situational mobile Web applications with Cloud-Mobile convergence: an internetware-oriented approach. IEEE Trans Serv Comput 12(1):6–19

    Article  Google Scholar 

  37. Mousavirad SJ, Ebrahimpour-Komleh H (2020) Human mental search-based multilevel thresholding for image segmentation. Applied Soft Comput 97:105427

Download references

Acknowledgments

This work was supported by the GDAS' Project of Building a World-class Research Institution in China under Grant 2020GDASYL-20200402007, GDAS' Project of Science and Technology Development under Grant 2018GDASCX-0115 and 2017GDASCX-0115.

Author information

Authors and Affiliations

  1. College of Computer and Information, City College of Dongguan University of Technology, Dongguan, 523419, Guangdong, China

    Ling Peng

  2. Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, 510000, Guangdong, China

    Dongbo Zhang

Authors
  1. Ling Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dongbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongbo Zhang.

Additional information

Publisher's Note

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

The original online version of this article was revised: In this article the affiliation details for author Ling Peng were incorrectly given.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, L., Zhang, D. An adaptive Lévy flight firefly algorithm for multilevel image thresholding based on Rényi entropy. J Supercomput 78, 6875–6896 (2022). https://doi.org/10.1007/s11227-021-04150-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-04150-3

Keywords

Search

Navigation