Skip to main content
SpringerLink
Log in
Book cover

International Conference on Applications and Techniques in Cyber Security and Intelligence

ATCI 2020: 2020 International Conference on Applications and Techniques in Cyber Intelligence pp 731–736Cite as

Segmentation Based on Particle Swarm Optimization

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1244))

Abstract

This paper aims to apply the particle swarm optimization (PSO) in the image segmentation. For this purpose, the popular segmentation methods were reviewed in details, the specific steps of the PSO was introduced, and the PSO-based image segmentation was examined in an experiment. The proposed method was contrasted with Ostu’s algorithm on the standard test image Lena. The results show that PSO-based image segmentation can create segments with great details. The findings of this study sheds new light on the application of the PSO and the image segmentation.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recogn. 13(1), 3–16 (1981)

    Article  MathSciNet  Google Scholar 

  2. Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)

    Article  Google Scholar 

  3. Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)

    Article  Google Scholar 

  4. Bansal, S., Maini, R.: A comparative analysis of iterative and Ostu’s thresholding techniques. Int. J. Comput. Appl. 66(12), 45–47 (2013)

    Google Scholar 

  5. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  6. Konishi, S., Yuille, A., Coughlan, J.: A statistical approach to multi-scale edge detection. Image Vis. Comput. 21(1), 37–48 (2003)

    Article  Google Scholar 

  7. Kanungo, T., Mount, D.M., Netanyahu, N.S.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002)

    Article  Google Scholar 

  8. Padmavathi, G., Muthukumar, M., Thakur, S.K.: Non linear image segmentation using fuzzy C means clustering method with thresholding for underwater images. Int. J. Comput. Sci. Issues. 7(3), 1–15 (2010)

    Google Scholar 

  9. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3), 825–838 (2007)

    Article  MATH  Google Scholar 

  10. Ben-Hur, D., Horn, H.T., Siegelmann, V., Vapnik, A.: Support vector clustering method. In: International Conference on Pattern Recognition, vol. 2, no. 2, pp. 724–727 (2000)

    Google Scholar 

  11. Kumar, L., Rath, S.K.: Application of genetic algorithm as feature selection technique in development of effective fault prediction model. In: 2016 IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering. IEEE (2016)

    Google Scholar 

  12. Reynolds, C.W.: Flocks, herds, and schools: a distributed behavioral model. Comput. Graph. 21(4), 25–34 (1987)

    Article  Google Scholar 

  13. Heppner, F., Grenander, U.: A Stochastic Nonlinear Model for Coordinated Bird Flocks. The Ubiquity of Chaos. American Association for the Advancement of Science, Washington, D.C. (1990)

    Google Scholar 

  14. Shi, Y., Eberhart, R.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation. IEEE (1999)

    Google Scholar 

  15. Shi, Y., Eberhart, R.: Parameter selection in particle swarm optimization. In: International Conference on Evolutionary Programming. Springer, Heidelberg (1998)

    Google Scholar 

  16. Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. IEEE (1995)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Innovation Program in Shaanxi Province of China (no. 2018KRM145), the Science Basic Research Program in Shaanxi Province of China (no. 16JK1823), the Natural Science Basic Research Plan in Shaanxi Province of China (no. 2017JM6086), the Education Scientific Program of 13th Five-year Plan in Shaanxi Province of China (no. SGH18H350), the Science Basic Research Program at the Xianyang Normal University of China (no. XSYK18011).

Author information

Authors and Affiliations

  1. School of Computer Science, Xianyang Normal University, Xianyang, Shaanxi, China

    Xiaoxue Song & Hong Li

Authors
  1. Xiaoxue Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoxue Song .

Editor information

Editors and Affiliations

  1. Distributed System and Security Research Cluster, Faculty of Science, Engineering and Built Environment, Deakin University, Geelong, VIC, Australia

    Jemal H. Abawajy

  2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

  4. University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Song, X., Li, H. (2021). Segmentation Based on Particle Swarm Optimization. In: Abawajy, J., Choo, KK., Xu, Z., Atiquzzaman, M. (eds) 2020 International Conference on Applications and Techniques in Cyber Intelligence. ATCI 2020. Advances in Intelligent Systems and Computing, vol 1244. Springer, Cham. https://doi.org/10.1007/978-3-030-53980-1_107

Download citation

Publish with us

Policies and ethics

Search

Navigation