Skip to main content
SpringerLink
Log in

Modified thermal exchange optimization based multilevel thresholding for color image segmentation

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

Abstract

This paper proposes a multi-threshold image segmentation method based on modified thermal exchange optimization (TEO). Although it is efficient and gives excellent result in the case of bi-level thresholding, but it takes a lot of computation when the number of threshold increases. To overcome this problem, the TEO algorithm is applied in this search area for searching the optimal thresholds. The Levy flight algorithm is employed to modify the original TEO and balance the exploration and exploitation of TEO. Experiments are conducted between six state-of-the-art metaheuristic algorithms and the proposed one. The benchmark functions, color nature images and stellite images are used in the experiment to test the performance of the algorithms involved. Qualitative experimental results show that the proposed segmentation approach has fewer iterations and higher segmentation accuracy.

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

Similar content being viewed by others

References

  1. Amirsadri S, Mousavirad SJ, Ebrahimpourkomleh H (2017) A levy flight-based grey wolf optimizer combined with back-propagation algorithm for neural network training. Neural Comput Applic 3–4:1–14

    Google Scholar 

  2. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  3. Bhambu P, Kumar S (2017) Levy flight based animal migration optimization algorithm. International conference on recent advances & innovations in engineering

  4. Bohat VK, Arya KV (2019) A new heuristic for multilevel thresholding of images. Expert Syst Appl 117:176–203

    Article  Google Scholar 

  5. Bozkurt ÖÖ, Biricik G, Tayşi ZC (2017) Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLoS One 12(4):e0175915

    Article  Google Scholar 

  6. Cai X, Gao XZ, Xue Y (2016) Improved bat algorithm with optimal forage strategy and random disturbance strategy. Inderscience Publishers

  7. Chakraborty S, Chatterjee S, Dey N et al (2017) Modified cuckoo search algorithm in microscopic image segmentation of hippocampus. Microsc Res Tech (3)

  8. Chaudhry A, Dokania PK, Torr PHS (2017) Discovering class-specific pixels for weakly-supervised semantic segmentation

  9. Cufoglu A, Lohi M, Everiss C (2017) Feature weighted clustering for user profiling. International Journal of Modeling Simulation & Scientific Computing 08(4):30–315

  10. Dong W, Li H, Wei X et al (2017) An efficient iterative thresholding method for image segmentation. J Comput Phys 350

  11. Gattenlöhner S, Gornyi IV, Ostrovsky PM et al (2016) Lévy flights due to anisotropic disorder in graphene. Phys Rev Lett 117(4):046603

    Article  Google Scholar 

  12. He L, Huang S (2017) Modified firefly algorithm based multilevel thresholding for color image segmentation. Neurocomputing 240:152–174

    Article  Google Scholar 

  13. Kan H, Yong Z, Bo L et al (2017) Salient object detection based on background feature clustering. Advances in Multimedia 2017(1):1–9

    Article  Google Scholar 

  14. Kaveh A, Dadras A (2017) A novel meta-heuristic optimization algorithm: thermal exchange optimization. Adv Eng Softw

  15. Kosiorowski D, Rydlewski JP, Snarska M (2017) Detecting a structural change in functional time series using local Wilcoxon statistic. Stat Pap 1–22

  16. Laux T, Otto F (2016) Convergence of the thresholding scheme for multi-phase mean-curvature flow. Calc Var 55(5):129

    Article  MathSciNet  Google Scholar 

  17. Li R, Liu W, Lei Y et al (2018) DeepUNet: a deep fully convolutional network for pixel-level sea-land segmentation. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 11:3954–3962

    Article  Google Scholar 

  18. Ma M, Zhu QQ (2017) Multilevel thresholding image segmentation based on shuffled frog leaping algorithm. J Comput Theor Nanosci 14(8):3794–3801

    Article  Google Scholar 

  19. Mirjalili S (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Applic 27(4):1053–1073

    Article  Google Scholar 

  20. Muangkote N, Sunat K, Chiewchanwattana S (2016) Multilevel thresholding for satellite image segmentation with moth-flame based optimization. International Joint Conference on Computer Science and Software Engineering IEEE 1–6

  21. Niharika E, Adeeba H, Krishna ASR et al (2017) K-means based noisy SAR image segmentation using median filtering and otsu method. International Conference on Iot & Application

  22. Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Gálvez J (2017) Cross entropy based thresholding for magnetic resonance brain images using crow search algorithm. Expert Syst Appl 79:164–180. https://doi.org/10.1016/j.eswa.2017.02.042

    Article  Google Scholar 

  23. Pare S, Bhandari AK, Kumar A et al (2018) Backtracking search algorithm for color image multilevel thresholding. SIViP 12(2):385–392

    Article  Google Scholar 

  24. Rajinikanth V, Satapathy SC (2018) Segmentation of ischemic stroke lesion in brain MRI based on social group optimization and fuzzy-Tsallis entropy. Arab J Sci Eng 43(35):1–14

    Google Scholar 

  25. Ren W, Pan J, Cao X et al (2017) Video deblurring via semantic segmentation and pixel-wise non-linear kernel

  26. Sampaio FC, Faria JTD, Silva GDDL et al (2017) Comparison of response surface methodology and artificial neural network for modeling xylose-to-xylitol bioconversion. Chem Eng Technol 40(1):122–129

    Article  Google Scholar 

  27. Somwanshi D, Kumar A, Sharma P et al (2017) An efficient brain tumor detection from MRI images using entropy measures. International conference on recent advances & innovations in engineering

  28. Tao PD, An TH (2017) A D.C. optimization algorithm for solving the trust-region subproblem. SIAM J Optim 8(2):476–505

    Article  MathSciNet  Google Scholar 

  29. Wang Z, Farhand S, Tsechpenakis G (2018) Fading affect bias: improving the trade-off between accuracy and efficiency in feature clustering. Machine Vision and Applications 30(2):255–268

    Article  Google Scholar 

  30. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82

    Article  Google Scholar 

  31. Wu D, Kim K, El GF et al (2017) Iterative low-dose CT reconstruction with priors trained by artificial neural network. IEEE Trans Med Imaging 99:1–1

    Google Scholar 

  32. Xuan TP, Siarry P, Oulhadj H (2018) Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation. Appl Soft Comput 65:230–242

    Article  Google Scholar 

  33. Xue J, He X, Yang X et al (2017) Multi-threshold image segmentation method based on flower pollination algorithm

  34. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Computer Knowledge & Technology. 284:65–74

    MATH  Google Scholar 

  35. Ye ZW, Wang MW, Liu W et al (2015) Fuzzy entropy based optimal thresholding using bat algorithm. Appl Soft Comput 31(C):381–395

    Article  Google Scholar 

  36. Zhou Y, Ling Y, Luo Q (2017) Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 5:6168–6186

    Article  Google Scholar 

  37. Zhu H, Zhuang Z, Zhou J et al (2017) Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization. Multimed Tools Appl 76(6):8951–8968

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, China

    Zhikai Xing & Heming Jia

Authors
  1. Zhikai Xing
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Heming Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heming Jia.

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

Xing, Z., Jia, H. Modified thermal exchange optimization based multilevel thresholding for color image segmentation. Multimed Tools Appl 79, 1137–1168 (2020). https://doi.org/10.1007/s11042-019-08229-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08229-1

Keywords

Search

Navigation