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Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation

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Abstract

Multilevel thresholding is a very important image processing technique in the field of image segmentation. However, the computational complexity of determining the optimal threshold grows exponentially with increasing thresholds. To overcome this drawback, in this paper, we propose a multi-threshold image segmentation method based on the moth swarm algorithm. The meta-heuristic algorithm uses Kapur’s entropy method to optimize the thresholds for eight standard test images. When compared with other state-of-the-art evolutionary algorithms, the proposed method proved to be robust and effective according to numerical experimental results and image segmentation results. This indicates the high performance of the method for the segmentation of digital images.

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References

  1. Bhandari AK, Singh VK, Kumar A, 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 

  2. 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 

  3. Develi I (2012) A new approximation based on the differential evolution algorithm for the gaussian q-function. Int J Innov Comput Inf Control Ijicic 8(10):7095–7102

    Google Scholar 

  4. Duraisamy SP, Kayalvizhi R (2010) A new multilevel thresholding method using swarm intelligence algorithm for image segmentation. J Intell Learn Syst Appl 2(3):126–128

    Google Scholar 

  5. El Aziz MA, Ewees AA, Hassanien AE (2017) Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 83:242–256

    Article  Google Scholar 

  6. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning, Machine Learning,3:95-99

  7. Hollander M, Wolfe DA, Chicken E (2014) Nonparametric statistical methods. Third Edition, John Wiley & Sons, Inc. Hoboken

  8. Jiang Y, Tsai P, Hao Z, Cao L (2015) Automatic multilevel thresholding for image segmentation using stratified sampling and Tabu Search. Soft Comput 19(9):2605–2617

    Article  Google Scholar 

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

    Article  Google Scholar 

  10. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization (Vol. 200). Technical report-tr06, Erciyes university, engineering faculty, computer engineering department

  11. Kennedy J (2010) Particle Swarm Optimization, Encyclopedia of Machine Learning. Springer, US, 760-766

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

    Article  Google Scholar 

  13. Lai CC, Tseng DC (2004) A hybrid approach using gaussian smoothing and genetic algorithm for multilevel thresholding. Int J Hybrid Intell Syst 1(3–4):143–152

    Google Scholar 

  14. Liu Y, Zhang X, Cui J, Wu C, Aghajan H, Zha H (2010) Visual analysis of child-adult interactive behaviors in video sequences. In Virtual Systems and Multimedia (VSMM) International Conference on, IEEE pp 26–33

  15. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. In Pattern Recognition (ICPR) International Conference on, IEEE pp 898–901

  16. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. In IJCAI, pp 1617–1623

  17. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115

    Article  Google Scholar 

  18. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing Complex Activities by a Probabilistic Interval-Based Model. In AAAI, pp 1266–1272

  19. Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tool Appl 76(8):10701–10719

    Article  Google Scholar 

  20. Maitra M, Chatterjee A (2008) A hybrid cooperative–comprehensive learning based pso algorithm for image segmentation using multilevel thresholding. Expert Syst Appl 34(2):1341–1350

    Article  Google Scholar 

  21. Mohamed AAA, Mohamed YS, El-Gaafary AA, Hemeida AM (2017) Optimal power flow using moth swarm algorithm. Electr Power Syst Res 142:190–206

    Article  Google Scholar 

  22. Otsu N (2007) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66

    Article  Google Scholar 

  23. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 38(9):1277–1294

    Article  Google Scholar 

  24. Pun T (1980) A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process 2(3):223–237

    Article  Google Scholar 

  25. Sarkar S, Sen N, Kundu A, Das S, Chaudhuri SS (2013) A differential evolutionary multilevel segmentation of near infra-red images using Renyi’s entropy. In Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) Springer, pp 699–706

  26. Shore J, Johnson R (1980) Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy. ieee trans. inform. theory it-26, 26-37. IEEE Trans Inf Theory 26(1):26–37

    Article  Google Scholar 

  27. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  28. Wang R, Zhou Y, Zhao C, Wu H (2015) A hybrid flower pollination algorithm based modified randomized location for multi-threshold medical image segmentation. Biomed Mater Eng 26(Suppl 1):S1345

    Google Scholar 

  29. Yang XS (2010) A new metaheuristic bat-inspired algorithm. Comput Knowl Technol 284:65–74

    MATH  Google Scholar 

  30. Yang XS (2013) Multiobjective firefly algorithm for continuous optimization. Eng Comput 29(2):175–184

    Article  Google Scholar 

  31. Yen JC, Chang FJ, Chang S (1995) A new criterion for automatic multilevel thresholding. IEEE Trans Image Process 4(3):370–378

    Article  Google Scholar 

  32. Yin PY (1999) A fast scheme for optimal thresholding using genetic algorithms. Signal Process 72(2):85–95

    Article  MATH  Google Scholar 

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Acknowledgments

This work was supported by the National Science Foundation of China under Grant No. 61463007 and the Project of the Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380264. We thank Maxine Garcia, PhD, from Liwen Bianji, Edanz Group China (www.liwenbianji.cn/ac) for editing the English text of a draft of this manuscript.

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Authors and Affiliations

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning, 530006, China

    Yongquan Zhou, Xiao Yang, Ying Ling & Jinzhong Zhang

  2. Key Laboratories of Guangxi High Schools Complex System and Computational Intelligence, Nanning, Guangxi, 530006, China

    Yongquan Zhou, Xiao Yang, Ying Ling & Jinzhong Zhang

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  1. Yongquan Zhou
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  2. Xiao Yang
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  3. Ying Ling
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  4. Jinzhong Zhang
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Correspondence to Yongquan Zhou.

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Zhou, Y., Yang, X., Ling, Y. et al. Meta-heuristic moth swarm algorithm for multilevel thresholding image segmentation. Multimed Tools Appl 77, 23699–23727 (2018). https://doi.org/10.1007/s11042-018-5637-x

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  • DOI: https://doi.org/10.1007/s11042-018-5637-x

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