A Supervised Figure-Ground Segmentation Method Using Genetic Programming

  • Yuyu LiangEmail author
  • Mengjie Zhang
  • Will N. Browne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9028)


Figure-ground segmentation is an important preprocessing phase in many computer vision applications. As different classes of objects require specific segmentation rules, supervised (or top-down) methods, which learn from prior knowledge of objects, are suitable for figure-ground segmentation. However, existing top-down methods, such as model-based and fragment-based ones, involve a lot of human work. As genetic programming (GP) can evolve computer programs to solve problems automatically, it requires less human work. Moreover, since GP contains little human bias, it is possible for GP-evolved methods to obtain better results than human constructed approaches. This paper develops a supervised GP-based segmentation system. Three kinds of simple features, including raw pixel values, six dimension and eleven dimension grayscale statistics, are employed to evolve image segmentors. The evolved segmentors are tested on images from four databases with increasing difficulty, and results are compared with four conventional techniques including thresholding, region growing, clustering, and active contour models. The results show that GP-evolved segmentors perform better than the four traditional methods with consistently good results on both simple and complex images.


Image segmentation Genetic programming Raw pixel values Grayscale statistics 


  1. 1.
    Zou, W., Bai, C., Kpalma, K., Ronsin, J.: Online glocal transfer for automatic figure-ground segmentation. IEEE Trans. Image Process. 23(5), 2109–2121 (2014)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Borenstein, E., Ullman, S.: Learning to segment. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 315–328. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  3. 3.
    Borenstein, E., Ullman, S.: Class-specific, top-down segmentation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 109–122. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  4. 4.
    Liu, J., Wang, J.: Application of snake model in medical image segmentation. J. Convergence Inf. Technol. 9(1), 105–109 (2014)Google Scholar
  5. 5.
    Liu, C.Y., Iglesias, J.E., Tu, Z.: Deformable templates guided discriminative models for robust 3D brain MRI segmentation. Neuroinformatics 11(4), 447–468 (2013)CrossRefGoogle Scholar
  6. 6.
    Borenstein, E., Sharon, E., Ullman, S.: Combining top-down and bottom-up segmentation. In: Proceedings IEEE workshop on Perceptual Organization in Computer Vision, CVPR 2004 (2004)Google Scholar
  7. 7.
    Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming (2008).
  8. 8.
    Poli, R.: Genetic Programming for feature detection and image segmentation. In: Fogarty, T.C. (ed.) AISB-WS 1996. LNCS, vol. 1143, pp. 110–125. Springer, Heidelberg (1996) CrossRefGoogle Scholar
  9. 9.
    Song, A., Ciesielski, V.: Fast texture segmentation using genetic programming. In: The 2003 Congress on Evolutionary Computation, pp. 2126–2133. IEEE (2003)Google Scholar
  10. 10.
    Song, A., Ciesielski, V.: Texture segmentation by genetic programming. Evol. Comput. 16(4), 461–481 (2008)CrossRefGoogle Scholar
  11. 11.
    Singh, T., Nawwaf, K., Mohmmad, D., Rabab, W.: Genetic programming based image segmentation with applications to biomedical object detection. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1123–1130. ACM (2009)Google Scholar
  12. 12.
    Roberts, M.E.: The effectiveness of cost based subtree caching mechanisms in typed genetic programming for image segmentation. In: Raidl, G.R., Cagnoni, S., Cardalda, J.J.R., Corne, D.W., Gottlieb, J., Guillot, A., Hart, E., Johnson, C.G., Marchiori, E., Meyer, J.-A., Middendorf, M. (eds.) EvoIASP 2003, EvoWorkshops 2003, EvoSTIM 2003, EvoROB/EvoRobot 2003, EvoCOP 2003, EvoBIO 2003, and EvoMUSART 2003. LNCS, vol. 2611, pp. 444–454. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  13. 13.
    Geng, J., Liu, J.: Image texture classification using a multiagent genetic clustering algorithm. In: Evolutionary Computation (CEC), pp. 504–508 (2011)Google Scholar
  14. 14.
    Luke, S.: The ECJ Owner’s Manual (2014)Google Scholar
  15. 15.
    Picard, R.W., Kabir, T., Liu, F.: Real-time recognition with the entire Brodatz texture database. In: IEEE Conference on CVPR, pp. 638–639 (1993)Google Scholar
  16. 16.
  17. 17.
    The PASCAL Visual Object Classes Homepage.
  18. 18.
    Powers, D.M.: Evaluation: from precision, recall and F-factor to ROC, informedness, markedness correlation. Technical report SIE-07-001, School of Informatics and Engineering, Flinders University, Australia (2007)Google Scholar
  19. 19.
    Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26(3), 839–851 (2005)CrossRefGoogle Scholar
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations