Advertisement

An Evolutionary and Graph-Based Method for Image Segmentation

  • Alessia Amelio
  • Clara Pizzuti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7491)

Abstract

A graph-based approach for image segmentation that employs genetic algorithms is proposed. An image is modeled as a weighted undirected graph, where nodes correspond to pixels, and edges connect similar pixels. A fitness function, that extends the normalized cut criterion, is employed, and a new concept of nearest neighbor, that takes into account not only the spatial location of a pixel, but also the affinity with the other pixels contained in the neighborhood, is defined. Because of the locus-based representation of individuals, the method is able to partition images without the need to set the number of segments beforehand. As experimental results show, our approach is able to segment images in a number of regions that well adhere to the human visual perception.

Keywords

Genetic Algorithm Image Segmentation Image Edge Meaningful Object Human Visual Perception 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen, C.W., Luo, J., Parker, K.J.: Image segmentation via adaptive k-means clustering and knowledge-based morphological operations with biomedical applications. IEEE Transactions on Image Processing 7(12), 1673–1683 (1998)CrossRefGoogle Scholar
  2. 2.
    Chen, S., Zhang, K.: Robust image segmentation using fcm with spatial constrains based on a new kernel-induced distance measure. IEEE Transactions on Systems Man and Cybernetics B 34, 1907–1916 (2004)CrossRefGoogle Scholar
  3. 3.
    Cour, T., Bénézit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005)), pp. 1124–1131 (2005)Google Scholar
  4. 4.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. John Wiley & Sons, New York (1973)zbMATHGoogle Scholar
  5. 5.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. International Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  6. 6.
    Di Gesú, V., Lo Bosco, G.: Image Segmentation Based on Genetic Algorithms Combination. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 352–359. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Halder, A., Pathak, N.: An evolutionary dynamic clustering based colour image segmentation. International Journal of Image Processing 4, 549–556 (2011)Google Scholar
  8. 8.
    Helterbrand, J.D.: One pixel-wide closed boundary identification. IEEE Transactions on Image Processing 5(5), 780–783 (1996)CrossRefGoogle Scholar
  9. 9.
    Jiao, L.: Evolutionary-based image segmentation methods. Image Segmentation (10), 180–224 (2011)Google Scholar
  10. 10.
    Lai, C.-C., Chang, C.-Y.: A hierarchical evolutionary algorithm for automatic medical image segmentation. Expert Syst. Appl. 36(1), 248–259 (2009)CrossRefGoogle Scholar
  11. 11.
    Leung, T., Malik, J.: Contour Continuity in Region Based Image Segmentation. In: Burkhardt, H.-J., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1406, pp. 544–559. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    Merzougui, M., Allaoui, A.E., Nasri, M., Hitmy, M.E., Ouariachi, H.: Evolutionary image segmentation by pixel classification and the evolutionary Xie and Beni criterion - application to quality control. International Journal of Computational Intelligence and Information Security 2(8), 4–13 (2011)Google Scholar
  13. 13.
    Pappas, T.N.: An adaptive clustering algorithms for image segmentation. IEEE Transactions on Signal Processing 40(4), 901–914 (1992)CrossRefGoogle Scholar
  14. 14.
    Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proc. of 3rd Annual Conference on Genetic Algorithms, pp. 2–9 (1989)Google Scholar
  15. 15.
    Paulinas, M., Uinskas, A.: A survey of genetic algorithms applications for image enhancement and segmentation. Information Technology And Control, Kaunas, Technologija 36(3), 278–284 (2007)Google Scholar
  16. 16.
    Sahoo, P.K., Soltani, S., Wong, A.K.C.: A survey of thresholding techniques. CVGIP 41, 233–260 (1988)Google Scholar
  17. 17.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)CrossRefGoogle Scholar
  18. 18.
    Urquhart, R.: Graph theoretical clustering based on limited neighborhood sets. Pattern Recognition 15(3), 173–187 (1982)MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and applications to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (1993)CrossRefGoogle Scholar
  20. 20.
    Xu, Y., Olman, V., Uberbacher, E.C.: A segmentation algorithm for noisy images: Design and evaluation. Pattern Recognition Letters 19, 1213–1224 (1998)zbMATHCrossRefGoogle Scholar
  21. 21.
    Zahn, C.T.: Graph theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20(1), 68–86 (1971)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alessia Amelio
    • 1
    • 2
  • Clara Pizzuti
    • 1
  1. 1.Institute for High Performance Computing and NetworkingNational Research Council of Italy, CNR-ICARRendeItaly
  2. 2.DEISUniversità della CalabriaRendeItaly

Personalised recommendations