Skip to main content
SpringerLink
Log in

An Automatic Image Segmentation Algorithm Based on Weighting Fuzzy C-Means Clustering

  • Conference paper
Soft Computing in Information Communication Technology

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 158))

Abstract

Image segmentation is an important research topic in the field of computer vision. Now the fuzzy C-Means (FCM) algorithm is one of the most frequently used clustering algorithms. Although a FCM algorithm is a clustering without supervising, the FCM arithmetic should be given the transcendent information of prototype parameter; otherwise the arithmetic will be wrong. This limits its application in image segmentation. In this paper, we develop a new theoretical approach to automatically selecting the weighting exponent in the FCM to segment the image, which is called Automatic Clustering Weighting Fuzzy C-Means Segmentation (ACWFCM). This method can reduce the disturbance of noise; get the segmentation numbers more accurately. The experimental results illustrate the effectiveness of the proposed method.

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 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Chen, C.W., Luo, J., Parker, K.J.: IEEE Trans. on Image Processing 7(12), 1673–1683 (1998)

    Article  Google Scholar 

  2. Parvati, K., Prakasa, R.S., Mariya, D.M.: Discrete Dynamics in Nature and Society, pp. 1–8 (2008)

    Google Scholar 

  3. Otman, B., Hongwei, Z., Fakhri, K.: Fuzzy Based Image Segmentation. Springer, Berlin (2003)

    Google Scholar 

  4. Lu, H., Zhang, L., Serikawa, S., et al.: A Method for Infrared Image Segment Based on Sharp Frequency Localized Contourlet Transform and Morphology. In: IEEE International Conference on Intelligent Control and Information Processing, Dalian, China, pp. 79–82 (2010)

    Google Scholar 

  5. Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Publishing House of Electronics Industry, Beijing (2004)

    Google Scholar 

  6. Han, J., Kamber, M.: Data mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2001)

    Google Scholar 

  7. Hung, M., Yang, D.: An Efficient Fuzzy C-Means Clustering Algorithm. In: IEEE Intel. Conf. on Data Mining, pp. 225–232 (2001)

    Google Scholar 

  8. Sun, J.X.: Image Processing. Science Press, Beijing (2005)

    Google Scholar 

  9. Xinbo, G., Jie, L., Hongbing, J.: A Multi-threshold Image Segmentation Algorithm Based on Weighting Fuzzy C-Means Clustering and Statistical Test. Acta Electronica Sinica 32(4), 661–664 (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Electronics, Kyushu Institute of Technology, E7-404, Kitakyushu, 804-8550, Japan

    Yujie Li, Huimin Lu, Lifeng Zhang, Shiyuan Yang & Seiichi Serikawa

  2. College of Information Engineering, Yangzhou University, Yangzhou, 225009, China

    Yujie Li, Junwu Zhu, Xuelong Hu, Xiaobin Zhang, Yun Li & Bin Li

Authors
  1. Yujie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huimin Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lifeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junwu Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shiyuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xuelong Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaobin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Seiichi Serikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yujie Li .

Editor information

Editors and Affiliations

  1. National Kinmen Institute of Technology, University Road, Jinning ownship, Kinmen, Taiwan R.O.C.

    Jia Luo

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag GmbH Berlin Heidelberg

About this paper

Cite this paper

Li, Y. et al. (2012). An Automatic Image Segmentation Algorithm Based on Weighting Fuzzy C-Means Clustering. In: Luo, J. (eds) Soft Computing in Information Communication Technology. Advances in Intelligent and Soft Computing, vol 158. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29148-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29148-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29147-0

  • Online ISBN: 978-3-642-29148-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation