Export file:


  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text


  • Citation Only
  • Citation and Abstract

Modified salp swarm algorithm based multilevel thresholding for color image segmentation

1 School of Mathematical Sciences, Harbin Normal University, Harbin 150025, China
2 College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China

Special Issues: Bio-inspired algorithms and Bio-systems

This paper proposes a multi-threshold image segmentation method based on modified salp swarm algorithm (SSA). Multi-threshold image segmentation method has good segmentation effect, but the segmentation precision will be affected with the increase of threshold number. To avoid the above problem, the slap swarm optimization algorithm (SSA) is presented to choose the optimal parameters of the fitting function and we use levy flight to improve the SSA. The solutions are assessed using the Kapur's entropy, Otsu and Renyi entropy fitness function during the optimization operation. The performance of the proposed algorithm is evaluated with several reference images and compared with different group algorithms. The results have been analyzed based on the best fitness values, peak signal to noise ratio (PSNR), and feature similarity index measures (FSIM). The experimental results show that the proposed algorithm outperformed other swarm algorithms.
  Article Metrics


1. Y. Feng, X. Shen, H. Chen, et al., Segmentation fusion based on neighboring information for MR brain images, Multimedia Tools Appl., 76 (2017), 23139-23161.

2. C. Wang, A. Y. Shi, X. Wang, et al., A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm, Optik, 125 (2014), 5588-5595.    

3. R. Gao and H. Wu, Agricultural image target segmentation based on fuzzy set, Optik, 126 (2015), 5320-5324.

4. K. Hammouche, M. Diaf and P. Siarry, A comparative study of various meta-heuristic techniques applied to the multilevel thresholding problem, Eng. Appl. Artif. Intell., 23 (2010), 676-688.

5. M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imaging, 13 (2004), 146-165.

6. A. García-Pedrero, C. Gonzalo-Martín and M. Lillo-Saavedra, A machine learning approach for agricultural parcel delineation through agglomerative segmentation, Int. J. Remote Sens., 38 (2017), 1809-1819.

7. D. Li, G. Zhai, X. Yang, et al., Perceptual information hiding based on multi-channel visual masking, Neurocomputing, 269 (2017), 170-179.

8. S. Yin, Y. Qian, and M. Gong, Unsupervised Hierarchical Image Segmentation through Fuzzy Entropy Maximization, Pattern Recognit., 68 (2017), 245-259.

9. S. Kumar, P. Kumar, T. K. Sharma, et al., Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method, Memetic Comput., 5 (2013), 323-334.

10. A. Colorni, M. Dorigo and V. Maniezzo, Distributed optimization by ant colonies, Proceedings of the first European conference on artificial life, 1992, 134-142. Available from: https://zz.glgoo.top/books?hl=zh-CN&lr=&id=pWsNJkdZ4tgC&oi=fnd&pg=PA134&dq=Distributed+Optimization+by+Ant+Colonies&ots=86J4mUqQSC&sig=_d2DgNHGaDzWKRQuxcGEhpNKRaI#v=onepage&q=Distributed%20Optimization%20by%20Ant%20Colonies&f=false.

11. W. Ding, C. Lin, S. Chen, et al., Multiagent-consensus-Map Reduce-based attribute reduction using co-evolutionary quantum PSO for big data applications, Neurocomputing, 272 (2018), 136-153.

12. R. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, Proceedings of the Sixth International Symposium on IEEE, 1995, 39-43. Available from: https://ieeexplore.ieee.org/document/494215.

13. K. Mistry, L. Zhang, S. C. Neoh, et al., A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition, IEEE Trans. Cybern., 47 (2017), 1496-1509.

14. R. Dong, J. Xu and B. Lin, ROI-based study on impact factors of distributed PV projects by LSSVM-PSO, Energy, 124 (2017), 336-349.

15. A. Fakhry, T. Zeng and S. Ji, Residual Deconvolutional Networks for Brain Electron Microscopy Image Segmentation, IEEE Trans. Med. Imaging, 36 (2017), 447-456.

16. D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J. Global Optim., 39 (2007), 459-471.

17. B. Jafrasteh and N. Fathianpour, Automatic extraction of geometrical characteristics hidden in ground-penetrating radar sectional images using simultaneous perturbation artificial bee colony algorithm, Geophys. Prospect., 65 (2017), 324-336.

18. Y. Zhang and L. Wu, Optimal multi-level thresholding based on maximum Tsallis entropy via an artificial bee colony approach, Entropy., 13 (2011), 841-859.

19. X. S. Yang, Firefly Algorithm, Lévy Flights and Global Optimization, Res. Dev. Intell. Syst. XXVI, 20 (2010), 209-218.

20. M. F. P. Costa, A. M. A. C. Rocha, R. B. Francisco, et al., Firefly penalty-based algorithm for bound constrained mixed-integer nonlinear programming, Optimization, 65 (2016), 1085-1104.

21. O. P. Verma, D. Aggarwal and T. Patodi, Opposition and dimensional based modified firefly algorithm, Expert Syst. Appl., 44 (2016), 168-176.

22. K. M. Sundaram, R. S. Kumar, C. Krishnakumar, et al., Fuzzy Logic and Firefly Algorithm based Hybrid System for Energy Efficient Operation of Three Phase Induction Motor Drives, Indian J. Sci. Technol., 9 (2016), 1-5.

23. X. S. Yang, A New Metaheuristic Bat-Inspired Algorithm, Nat. Inspired Coop. Strategies Optim., 284 (2010), 65-74.

24. H. Liang, Y. Liu, Y. Shen, et al., A Hybrid Bat Algorithm for Economic Dispatch with Random Wind Power, IEEE Trans. Power Syst., 99 (2018), 5052-5061.

25. A. Mumtaz, R. Deo, N. Downs, et al., Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting, Atmos. Res., 213 (2018), 450-464.

26. Y. Yuan, X. Wu, P. Wang, et al., Application of improved bat algorithm in optimal power flow problem, Appl. Intell., 48 (2018), 2304-2314.    

27. K. Kaced, C. Larbes, N. Ramzan, et al., Bat algorithm based maximum power point tracking for photovoltaic system under partial shading conditions, Sol. Energy, 158 (2017), 490-503.

28. S. Mirjalili and A. Lewis, The Whale Optimization Algorithm, Adv. Eng. Software, 95 (2016), 51-67.

29. R. Gupta, S. Ruosaari, S. Kulathinal, et al., Microarray image segmentation using additional dye-An experimental study, Mol. Cell. Probes, 21 (2007), 321-328.

30. O. Diego, M. A. E. Aziz and A. E. Hassanien, Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm, Appl. Energy, 200 (2017), 141-154.

31. N. Nahas, A. Khatab, D. Ait-Kadi, et al., Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems, Reliab. Eng. Syst. Saf., 93 (2008), 1658-1672.

32. D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput., 1 (1997), 67-82.

33. R. A. Ibrahim, A. A. Ewees, D. Oliva, et al., Improved salp swarm algorithm based on particle swarm optimization for feature selection, J. Ambient Intell. Humanized Comput., 10 (2019), 3155-3169.

34. A. G. Hussien, A. E. Hassanien and E. H. Houssein, Swarming Behaviour of Salps Algorithm for Predicting Chemical Compound Activities, 2017 Eighth International Conference on Intelligent Computing and Information Systems, Egypt, 2018. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/8260072.

35. G. I. Sayed, G. Khoriba and M. H. Haggag, A novel chaotic salp swarm algorithm for global optimization and feature selection, Appl. Intell.,48 (2018), 3462-3481.

36. M. H. Qais., H. M. Hasanien and S. Alghuwainem, Enhanced salp swarm algorithm: Application to variable speed wind generators, Eng. Appl. Artif. Intell., 80 (2019), 82-96.

37. M. Sezgin and B. Sankur, Survey over image thresholding techniques and quantitative performance evaluation, J. Electron. Imaging, 13 (2004), 146-166.

38. T. Pun, A New Method for Gray-Level Picture Thresholding Using the Entropy of the Histogram, Signal Process., 2 (1980), 223-237.

39. N. Otsu, Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Syst. Man Cybern., 9 (1979), 62-66.

40. M. Subrahmanyam, Q. M. J. Wu, R. P. Maheshwari, et al., Modified color motif cooccurrence matrix for image indexing and retrieval, Comput. Electr. Eng., 39 (2013), 762-774.

41. H. Gao, C. Pun and K. Sam, An efficient image segmentation method based on a hybrid particle swarm algorithm with learning strategy, Inf. Sci., 369 (2016), 500-521.

42. U. Kandaswamy, D. A. Adjeroh and M. C. Lee, Efficient texture analysis of SAR imagery, IEEE Trans. Geosci. Remote Sens., 43 (2005), 2075-2083.

43. K. S. Tan and N. A. M. Isa, Color image segmentation using histogram thresholding-Fuzzy C-means hybrid approach, Pattern Recognit., 44 (2011), 1-15.

44. B. Akay, A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding, Appl. Soft Comput., 13 (2013), 3066-3091.

45. A. K. Bhandari, A. Kumar, S. Chaudhary, et al., A novel color image multilevel thresholding based segmentation using nature inspired optimization algorithms, Expert Syst. Appl., 63 (2016), 112-133.

46. S. Pare, A. K. Bhandari, A. Kumar, et al., An optimal Color Image Multilevel Thresholding Technique using Grey-Level Co-occurrence Matrix, Expert Syst. Appl., 87 (2017), 335-362.

47. A. K. Bhandari, V. K. Singh, A. Kumar, et al., Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur's entropy, Expert Syst. Appl., 41 (2014), 3538-3560.

48. A. K. M. Khairuzzaman and S. Chaudhury, Multilevel thresholding using grey wolf optimizer for image segmentation, Expert Syst. Appl., 86 (2017), 64-76.

49. H. Liang, H. Jia, Z. Xing, et al., Modified Grasshopper Algorithm-Based Multilevel Thresholding for Color Image Segmentation, IEEE Access, 7 (2019), 11258-11295.

50. L. He and S. Huang, Modified firefly algorithm based multilevel thresholding for color image segmentation, Neurocomputing, 240 (2017), 152-174.

51. Z. Xing and H. Jia, Multilevel Color Image Segmentation Based on GLCM and Improved Salp Swarm Algorithm, IEEE Access, 7 (2019), 37672-37690.

52. S. Mishra and M. Panda, Bat Algorithm for Multilevel Colour Image Segmentation Using Entropy-Based Thresholding, Arabian J. Sci. Eng., 43 (2018), 1-30.

53. M. Z. Ali, N. H. Awad., G. R. Robert, et al., A balanced Fuzzy Cultural Algorithm with a Modified Levy Flight Search for Real Parameter Optimization, Inf. Sci., 447 (2018), 12-35.

54. A. A. Dubkov, B. Spagnolo and V. V. Uchaikin, Levy Flight Superdiffusion: An Introduction, Int. J. Bifurcation Chaos, 18 (2008), 2649-2672.

55. R. Li and Y. Wang, Improved Particle Swarm Optimization Based on Lévy Flights, J. Syst. Simul., 29 (2017), 1685-1691.

56. A. Mesa, K. Castromayor, C. Garillos-Manliguez, et al., Cuckoo search via Levy flights applied to uncapacitated facility location problem, J. Ind. Eng. Int., 14 (2018), 585-592.    

57. S. J. Mousavirad, H. Ebrahimpour-Komleh, Human mental search: A new population-based metaheuristic optimization algorithm, Appl. Intell., 47 (2017), 850-887.

58. P. D. Sathya and R. Kayalvizhi, Modified bacterial foraging algorithm based mul-tilevel thresholding for image segmentation, Expert Syst. Appl., 24 (2011), 595-615.

59. S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, et al., Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems, Adv. Eng. Software, 114 (2017), 13-48.

60. I. Pavlyukevich, Lévy flights, non-local search and simulated annealing, J. Comput. Phys., 226 (2007), 1830-1844.

61. P. Imkeller and I. Pavlyukevich, Lévy flights: Transitions and meta-stability, J. Phys. A Math. Gen., 39 (2006), 237-246.

62. Z. Chen, T. J. Feng and Z. Houkes, Texture segmentation based on wavelet and Kohonen network for remotely sensed images, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028), 1999, 816-821. Available from: https://ieeexplore_ieee.gg363.site/abstract/document/816656.

63. M. Cuinin, Segmentation 3D des organes à risque du tronc masculin à partir d'images anatomiques TDM et IRM à l'aide de méthodes hybrids (in French), Normandie, 98 (2017), 1188.

64. H. Jia, Z. Xing, W. Song, Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation, Remote Sens., 11 (2019), 1046.

65. F. Wilcoxon, Individual comparisons by ranking methods, Breakthroughs Stat., 1992 (1992), 196-202.

© 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Article outline

Show full outline
Copyright © AIMS Press All Rights Reserved