Skip to main content
SpringerLink
Log in

Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Since the infrared detector itself is subject to various external disturbances when collecting information, infrared images are characterized by of low SNR, low contrast and blur edge, which greatly increases the difficulty of detection and recognition. Contraposing the problems that a fuzzy clustering algorithm cannot find the reasonable clustering number adaptively, it will have a low infrared image segmentation rate when the gray-scale between object region and background region are of great difference. A gray-scale adaptive fuzzy clustering algorithm (GAFC) is proposed in this work. The methodology uses a coarse-fine concept to reduce the computational burden required for the fuzzy clustering and to improve the accuracy of segmentation that a single fuzzy clustering cannot reach. The coarse segmentation attempts to segment coarsely based on gray level histogram. Firstly, the pseudo peaks in the gray level histogram are removed by introducing a control factor of peak areas and a control factor of peak widths, then in order to find a finer segmentation result, the coarse segmentation result is clustered by an improved fuzzy clustering algorithm that introduces an adaptive function to get the most reasonable cluster number and that defines a logarithmic function as a measurement of distance. The results of experimental data show that not only the GAFC mentioned in this paper preserves the advantages in multi-threshold segmentation method which is fast and easy, and behaves well in segmenting infrared images in complex environments.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Balla-Arabé S, Gao XB, Wang B (2013) A Fast and Robust Level Set Method for Image Segmentation Using Fuzzy Clustering and Lattice Boltzmann Method. IEEE Trans Cybern 43(3):910–920

    Article  Google Scholar 

  2. Chabrier, S., Laurent, H., Rosenberger, C., Zhang, Y.J.: (2006) Supervised Evaluation of Synthetic and Real Contour Segmentation Results, In: 14th European Signal Processing Conference, pp. 1143–1146, Florence

  3. Chen H.P., Shen X.J., Long J.W. (2015) Histogram-based colour image fuzzy clustering algorithm. Multimedia Tools and Applications, 1–16, Springer, New York

  4. Cui ZH, Sun, Chen SG, Gao LQ (2014) Mean shift based FCM image segmentation algorithm. J control Decis 29(6):1130–1134

    Google Scholar 

  5. Harris C, Stephens M. (1988) A Combined Corner and Edge Detection[C]. Proceedings of the Fourth Alvey Vision Conference, The Plessey Company pic, London, 147–151.

  6. Kubassova O., Boesen M., Bliddal H. (2008) General Framework for Unsupervised Evaluation of Quality of Segmentation Results. In: 15th IEEE International Conference on Image Processing, pp. 3036–3039, San Diego

  7. Li Y, Yu FS (2009) A New Validity Function for Fuzzy Clustering. In: 9th IEEE International Conference on Computational Intelligence and Natural Computing. IEEE Press, Wuhan, pp. 462–465

    Google Scholar 

  8. Li JS, Yang W, Zhang XM (2009) Infrared Image Processing, Analysis and Fusion. Science press, Beijing

    Google Scholar 

  9. Li JP, Fu LQ, Han Y (2010) A Method of Object Segmentation in Complex Environment. Journal of Projectiles, Rockets, Missiles and Guidance 30(4):197–200

    Google Scholar 

  10. Lim YW, Lee SU (1990) On the color image segmentation algorithm based on the Thresholding and the fuzzy C-means techniques. Pattern Recogn 23(9):935–952

    Article  Google Scholar 

  11. Du X.C, Liu J.P. The infrared image segmentation method based on two-dimensional OTSU and genetic algorithm. Infrared Technology 27(1): 66–69 (2005)

  12. Liu J., Wang H.Y., Wang S.H. Infrared image segmentation using adaptive FCM algorithm based on potential function. TELKOMNIKA Indonesian J Electr Eng 12(8): 6230–6237(2014)

  13. Lopes N V, Couto P A, Bustince H, et al. Automatic histogram threshold using fuzzy measures[J]. IEEE Trans Image Process 19(1): 199–204 (2010)

  14. Medina C R, Carmona P A, Determining hysteresis thresholds for edge detection by combining the advantages and disadvantages of Thresholding methods[J]. IEEE Trans Image Process 19(1): 165–173 (2010)

  15. Pedrycz W (2005) Knowledge-based Clustering: from Data to Information Granules. John Wiley & Sons, Hoboken

    Book  MATH  Google Scholar 

  16. Tan HB, Hou ZQ, Liu R (2010) Region growing image segmentation based on human visual model. J Image and Graphics 15(9):1352–1356

    Google Scholar 

  17. Tang Q (2010) Threshold segmentation and the infrared image pedestrian detection research[D]. South China University of Technology, Guangzhou

    Google Scholar 

  18. Tao TF, Han CZ, Dai XF (2004) Integrated edge detection and region growing method of infrared image segmentation[J]. Photoelectric Engineering 31(10):50–52

    Google Scholar 

  19. The OTCBVS Benchmark Dataset (2004) http://www.vcipl.okstate.edu/otcbvs/bench/

  20. Unnikrishnan R, Pantofaru C, Hebert M (2007) Toward objective evaluation of image segmentation algorithms. IEEE Trans Pattern Anal Mach Intell 29(6):929–944

    Article  Google Scholar 

  21. Wang SH (2013) Research on Methods for Infrared Image Target Segmentation. Xidian University, Xi’an

    Google Scholar 

  22. Wang HY (2014) Research on Image Target Segmentation Methods Based on Clustering. Xidian University, Xi’an

    Google Scholar 

  23. Wang L, Yung N H C. Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation[J]. IEEE Trans Intell Transp Syst 11(1): 40–51(2010)

  24. Witkin AP (1984) Scale-space filtering: A New Approach to Multi-scale Description. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE Press, California, pp. 150–153

    Google Scholar 

  25. Wu KL, Yang MS (2002) Alternative c-means clustering algorithms. Pattern Recogn 35(10):2267–2278

    Article  MATH  Google Scholar 

  26. Wu R, Huang JH, Tang XL (2009) Based on the gray-level histogram and spectral clustering text image binarization method[J]. Journal of Electronics and Information 31(10):2460–2464

    Google Scholar 

  27. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847

    Article  Google Scholar 

  28. Xu LP (2007) Digital Image Processing. Science Press, Beijing

    Google Scholar 

Download references

Acknowledgments

This research was supported in part by the National Natural Science Foundation of China (Grant No. 61101246) and the Fundamental Research Funds for the Central Universities (Grant No. JB150209).

Author information

Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jin Liu, Yanli Liu & Qianqian Ge

Authors
  1. Jin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanli Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qianqian Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jin Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, J., Liu, Y. & Ge, Q. Infrared image segmentation based on gray-scale adaptive fuzzy clustering algorithm. Multimed Tools Appl 76, 11111–11125 (2017). https://doi.org/10.1007/s11042-016-3657-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3657-y

Keywords

Search

Navigation