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An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy

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Abstract

Multilevel thresholding image segmentation has attracted a lot of attention in the last several years since it has plenty of applications. The traditional exhaustive search methods are efficient for bi-level thresholding. However, they are time-consuming when extended to multilevel thresholding. To tackle this problem, a novel adaptive firefly algorithm (AFA) for multilevel thresholding using the minimum cross-entropy as its objective function has been proposed in this paper. The performance of the proposed algorithm has been examined on a set of benchmark images using various numbers of thresholds and has been compared with five different firefly variant algorithms. The experimental results indicated that the proposed algorithm outperformed the other five algorithms in terms of image segmentation quality, accuracy, and computation time.

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References

  1. Houssein EH, Helmy BE, Oliva D et al (2021) Multi-level thresholding image segmentation based on nature-inspired optimization algorithms: a comprehensive review. Metaheuristics in Mach Learn: Theory Appl 239–265

  2. 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 

  3. Pare S, Kumar A, Singh GK et al (2020) Image segmentation using multilevel thresholding: a research review. Iran J Sci Technol Trans Electr Eng 44(1):1–29

    Article  Google Scholar 

  4. Abdel-Basset M, Chang V, Mohamed R (2021) A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems[J]. Neural Comput Appl 33(17):10685–10718

    Article  Google Scholar 

  5. Akay B (2013) A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Appl Soft Comput 13(6):3066–3091

    Article  Google Scholar 

  6. Li Y, Bai X, Jiao L et al (2017) Partitioned-cooperative quantum-behaved particle swarm optimization based on multilevel thresholding applied to medical image segmentation. Appl Soft Comput 56:345–356

    Article  Google Scholar 

  7. Alihodzic A, Tuba M (2014) Improved Bat Algorithm Applied to Multilevel Image Thresholding. Scientific World Journal 2014:176718–176718

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Bhandari AK, Singh VK, Kumar A et al (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 

  11. Li L, Sun L, Guo J et al (2017) Modified discrete grey wolf optimizer algorithm for multilevel image thresholding. Comput Intell Neurosci 2017:1–16

    Google Scholar 

  12. Aziz MA, Ewees AA, Hassanien AE et al (2017) Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  13. He L, Huang S (2020) An efficient krill herd algorithm for color image multilevel thresholding segmentation problem. Appl Soft Comput 89:106063

    Article  Google Scholar 

  14. Sharma A, Chaturvedi R, Kumar S et al (2020) Multi-level image thresholding based on Kapur and Tsallis entropy using firefly algorithm. J Interdiscip Math 23(2):563–571

    Article  Google Scholar 

  15. Houssein EH, Helmy BE, Oliva D et al (2021) A novel Black Widow Optimization algorithm for multilevel thresholding image segmentation. Expert Syst Appl 167:114159

    Article  Google Scholar 

  16. Tan Z, Zhang D (2020) A fuzzy adaptive gravitational search algorithm for two-dimensional multilevel thresholding image segmentation. J Ambient Intell Humaniz Comput 11(11):4983–4994

    Article  Google Scholar 

  17. Srikanth R, Bikshalu K (2021) Multilevel thresholding image segmentation based on energy curve with harmony Search Algorithm. Ain Shams Eng J 12(1):1–20

    Article  Google Scholar 

  18. 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 

  19. Ewees AA, Abualigah L, Yousri D et al (2021) Modified artificial ecosystem-based optimization for multilevel thresholding image segmentation. Mathematics 9(19):2363

    Article  Google Scholar 

  20. Pan X, Xue L, Li R (2019) A new and efficient firefly algorithm for numerical optimization problems. Neural Comput Appl 31(5):1445–1453

    Article  Google Scholar 

  21. Al-Janabi S, Alkaim A, Al-Janabi E et al (2021) Intelligent forecaster of concentrations (PM2.5, PM10, NO2, CO, O3, SO2) caused air pollution (IFCsAP). Neural Comput Appl 33:1–31

    Article  Google Scholar 

  22. Abdullah MN, Abdullah NA, Aswan NF et al (2019) Combined economic-emission load dispatch solution using firefly algorithm and fuzzy approach. Indon J Electr Eng Comput Sci 16(1):127–135

    Google Scholar 

  23. Alkaim AF, Al-Janabi S (2019) Multi objectives optimization to gas flaring reduction from oil production. In: International Conference on Big Data and Networks Technologies. Springer, Cham, pp 117–139

  24. Yang X (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-inspired Comput 2(2):78–84

    Article  Google Scholar 

  25. Raja NS, Manic KS, Rajinikanth V et al (2013) Firefly algorithm with various randomization parameters: an analysis. Swarm evolutionary and memetic computing, pp 110–121

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

    Article  Google Scholar 

  27. Fister I, Perc M, Kamal SM et al (2015) A review of chaos-based firefly algorithms. Appl Math Comput 252:155–165

    Article  MathSciNet  Google Scholar 

  28. Yu S, Zhu S, Ma Y et al (2015) A variable step size firefly algorithm for numerical optimization. Appl Math Comput 263:214–220

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  30. Lei B, Fan J (2020) Multilevel minimum cross entropy thresholding: A comparative study. Appl Soft Comput 96:106588

    Article  Google Scholar 

  31. Sathya PD, Kalyani R, Sakthivel VP (2021) Color image segmentation using Kapur, Otsu and Minimum Cross Entropy functions based on Exchange Market Algorithm. Expert Syst Appl 172:114636

    Article  Google Scholar 

  32. Arbelaez P, Maire M, Fowlkes C et al (2010) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  33. Lin B, Huang Y, Zhang J, Junqin Hu, Chen* X, Li* J (2020) Cost-driven offloading for dnn-based applications over cloud, edge and end devices. IEEE Trans Ind Inform 16(8):5456–5466

  34. Chen X, Lin J, Ma Y, Lin B, Wang H, Huang* G (2019) Self-adaptive resource allocation for cloud-based software services based on progressive QoS prediction model. SCIENCE CHINA Inform Sci 62(11):219101

  35. 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 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (31671591); Guangdong Provincial Special Fund For Modern Agriculture Industry Technology Innovation Teams (2021KJ108); Natural Science Foundation of Guangdong Province of China (2020A1515010784); China Agriculture Research System of MOF and MARA (CARS-26) and Guangdong Youth Characteristic Innovation Project (2021KQNCX120).

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Authors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Yi Wang & Shuran Song

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  1. Yi Wang
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  2. Shuran Song
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Correspondence to Shuran Song.

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Wang, Y., Song, S. An adaptive firefly algorithm for multilevel image thresholding based on minimum cross-entropy. J Supercomput 78, 11580–11600 (2022). https://doi.org/10.1007/s11227-021-04281-7

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