Advertisement

Pattern Recognition and Image Analysis

, Volume 25, Issue 1, pp 89–95 | Cite as

An automatic image segmentation algorithm involving shortest path basins

  • T. RybaEmail author
  • M. Zelezny
Representation, Processing, Analysis and Understanding of Images

Abstract

Image segmentation is a process of partitioning input image into meaningful regions. It is a challenging task that is involved in almost every image processing system. Currently lot of methods for image segmentation with different approaches was created. Between all of them the methods based on graph theory are more and more popular nowadays. Segmentation methods could be classified for example to interactive and automatic ones. The further class of methods benefits from a user interaction that provides valuable information about a segmentation problem. The later class of methods doesn’t incorporate any user interaction. Nevertheless fully automatic methods that are both precise and robust are still hard to find. In this paper a new method based on shortest path in a graph is presented. This method automatically places seed points that are further used for image segmentation in the sense of path basins. This method allows segment an input image to a predefined or to an undefined number of image segments. Derived seed points could also be used in other interactive methods instead of a user interaction. Experiments with this method show its potential for segmenting a general class of images.

Keywords

image segmentation shortest path in a graph 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    B. Peng, L. Zhang, and D. Zhang, “A survey of graph theoretical approaches to image segmentation,” Pattern Recognition 46(3), 1020–1038 (2013).CrossRefGoogle Scholar
  2. 2.
    P. F. Felzenszwalb and D. P. Huttenlocher, “Efficient graph based image segmentation,” Int. J. Computer Vision 59(2), 167–181 (2004).CrossRefGoogle Scholar
  3. 3.
    J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intellig. 22(8), 888–905 (2000).CrossRefGoogle Scholar
  4. 4.
    I. J. Cox, S. B. Rao, and Y. Zhong, “Ratio regions: a technique for image segmentation,” in Proc. Int. Conf. on Pattern Recognition (San Francisco, 1996), pp. 557–564.CrossRefGoogle Scholar
  5. 5.
    L. Grady, “Random walks for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intellig. 28(11), 1768–1783 (2006).CrossRefGoogle Scholar
  6. 6.
    M. Wertheimer, “Laws of organization in perceptual forms (partial translation),” in A Sourcebook of Gestalt Psychology (Routledge and Kegan Paul, London, 1938), pp. 71–88.CrossRefGoogle Scholar
  7. 7.
    E. W. Dijkstra, “A note on two problems in connection with graphs,” Num. Math. 1(1), 269–271 (1959).CrossRefzbMATHMathSciNetGoogle Scholar
  8. 8.
    Y. Boykov and G. Funka-Lea, “Graph cuts and efficient N-D image segmentation,” Int. J. Computer Vision 70(2), 109–131 (2006).CrossRefGoogle Scholar
  9. 9.
    E. N. Mortensen and W. A. Barrett, “Intelligent scissors for image composition,” in Proc. 22nd Annu. Conf. on Computer Graphics and Interactive Techniques SIGGRAPH 95, (Los Angeles, 1995), pp. 191–198.CrossRefGoogle Scholar
  10. 10.
    A. X. Falcao, J. K. Udupa, and F. K. Miyazawa, “An ultra-fast user-steered image segmentation paradigm: live wire on the fly,” IEEE Trans. Med. Imag. 19(1), 55–62 (2000).CrossRefGoogle Scholar
  11. 11.
    M. Pavan and M. Pelillo, “A new graph-theoretic approach to clustering and segmentation,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (Madison, 2003), pp. 145–152.Google Scholar
  12. 12.
    C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: interactive foreground extraction using iterated graph cuts,” ACM Trans. Graph. 23, 309–314 (2004).CrossRefGoogle Scholar
  13. 13.
    S. Beucher, “Watershed, hierarchical segmentation and waterfall algorithm,” in Mathematical Morphology and Its Applications to Image Processing (Kluwer, Dordrecht, 1994), pp. 69–76.CrossRefGoogle Scholar
  14. 14.
    R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, “SLIC Superpixels Compared to State-of the-art Superpixel Methods,” Trans. PAMI 34(11), 2274–2282 (2012).CrossRefGoogle Scholar
  15. 15.
    C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proc. 6th Int. Conf. on Computer Vision (Bombay, 1998).Google Scholar
  16. 16.
    A. Chambolle, “An algorithm for total variation minimization and applications,” J. Math. Imaging Vision 20, 89–97 (2004).CrossRefMathSciNetGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2015

Authors and Affiliations

  1. 1.The University of West BohemiaPilsenCzech Republic

Personalised recommendations