Skip to main content
SpringerLink
Log in

A Multi-Level Thresholding Image Segmentation Based on an Improved Artificial Bee Colony Algorithm

  • Conference paper
  • First Online:
2nd EAI International Conference on Robotic Sensor Networks

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

  • 567 Accesses

Abstract

As a popular evolutionary algorithm, artificial bee colony (ABC) algorithm has been successfully applied into the threshold-based image segmentation problem. Based on our analysis, we find that the Otsu segmentation function is separable which means each variable is independent. Due to its one-dimensional search strategy and relative power global but poorer local search abilities, ABC could find an acceptable but not precise segmentation results. For making more precise search and further enhancing the achievements on image segmentation, we propose an Otsu segmentation method based on a new ABC algorithm with an improved scout bee strategy. Different from the traditional scout bee strategy, we use a local search strategy when a bee stagnates for a defined value. The experimental results on Berkeley segmentation database demonstrate the effectiveness of our algorithm.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  1. Mylonas, S. K., Stavrakoudis, D. G., Theocharis, J. B., et al. (2016). A local search-based GeneSIS algorithm for the segmentation and classification of remote-sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(4), 1470–1492.

    Article  Google Scholar 

  2. Pinheiro, M., & Alves, J. L. (2015). A new level-set-based protocol for accurate bone segmentation from CT imaging. IEEE Access, 3, 1894–1906.

    Article  Google Scholar 

  3. Dang, C., Gao, J., Wang, Z., et al. (2015). Multi-step radiographic image enhancement conforming to weld defect segmentation. IET Image Processing, 9(11), 943–950.

    Article  Google Scholar 

  4. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.

    Article  Google Scholar 

  5. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06. Kayseri: Erciyes University.

    Google Scholar 

  6. Omidvar, M. N., Li, X., Mei, Y., & Yao, X. (2014). Cooperative co-evolution with differential grouping for large scale optimization. IEEE Transactions on Evolutionary Computation, 18(3), 378–393.

    Article  Google Scholar 

  7. Akay, B. (2013). A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding. Applied Soft Computing, 13(6), 3066–3091.

    Article  Google Scholar 

  8. Cao, L. L., Ding, S., Fu, X. W., et al. (2016). Otsu multilevel thresholding segmentation based on quantum particle swarm optimisation algorithm. International Journal of Wireless and Mobile Computing, 10(3), 272–277.

    Article  Google Scholar 

  9. Sarkar, S., & Das, S. (2013). Multilevel image thresholding based on 2D histogram and maximum Tsallis entropy—A differential evolution approach. IEEE Transactions on Image Processing, 22(12), 4788–4797.

    Article  MathSciNet  Google Scholar 

  10. Kumar, S., Kumar, P., Sharma, T. K., et al. (2013). Bi-level thresholding using PSO, artificial bee colony and MRLDE embedded with Otsu method. Memetic Computing, 5(4), 323–334.

    Article  MathSciNet  Google Scholar 

  11. Horng, M. H. (2011). Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation. Expert Systems with Applications, 38(11), 13785–13791.

    Google Scholar 

  12. Bhandari, A. K., Kumar, A., & Singh, G. K. (2015). Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications, 42(3), 1573–1601.

    Article  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the support from National Nature Science Foundation of China (No. 61571236, 61533010, 61320106008, 61602255) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0795).

Author information

Authors and Affiliations

  1. The Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Xingyu Xia & Hao Gao

  2. Department of Computer and Information Science, University of Macau, Macau SAR, China

    Hao Gao & Chi-Man Pun

  3. Beijing Institute of Control Engineering, Beijing, China

    Haidong Hu

  4. Key Laboratory of Intelligent Processing of Computer Image and Graphics, Guilin University of Electronic Technology, Guilin, China

    Rushi Lan

Authors
  1. Xingyu Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haidong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rushi Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chi-Man Pun
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. Department of Electronics Engineering and Computer Science, Fukuoka University, Fukuoka, Japan

    Li Yujie

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xia, X., Gao, H., Hu, H., Lan, R., Pun, CM. (2020). A Multi-Level Thresholding Image Segmentation Based on an Improved Artificial Bee Colony Algorithm. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-17763-8_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17762-1

  • Online ISBN: 978-3-030-17763-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation