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Improving Graph-Based Image Segmentation Using Automatic Programming

  • Lars Vidar MagnussonEmail author
  • Roland Olsson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

This paper investigates how Felzenszwalb’s and Huttenlocher’s graph-based segmentation algorithm can be improved by automatic programming. We show that computers running Automatic Design of Algorithms Through Evolution (ADATE), our system for automatic programming, have induced a new graph-based algorithm that is 12 percent more accurate than the original without affecting the runtime efficiency. The result shows that ADATE is capable of improving an effective image segmentation algorithm and suggests that the system can be used to improve image analysis algorithms in general.

Keywords

Image segmentation Graph algorithm Evolutionary computation Automatic programming 

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References

  1. 1.
    Alpert, S., Galun, M., Basri, R., Brandt, A.: Image segmentation by probabilistic bottom-up aggregation and cue integration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (June 2007)Google Scholar
  2. 2.
    Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 898–916 (2011)CrossRefGoogle Scholar
  3. 3.
    Berg, H., Olsson, R., Lindblad, T., Chilo, J.: Automatic design of pulse coupled neurons for image segmentation. Neurocomputing 71(10-12), 1980–1993 (2008); Neurocomputing for Vision Research; Advances in Blind Signal ProcessingGoogle Scholar
  4. 4.
    Berg, H., Olsson, R., Rusas, P.O., Jakobsen, M.: Synthesis of control algorithms for autonomous vehicles through automatic programming. In: Proceedings of the 2009 Fifth International Conference on Natural Computation, ICNC 2009, vol. 4, pp. 445–453. IEEE Computer Society (2009)Google Scholar
  5. 5.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  6. 6.
    Felzenszwalb, P., Huttenlocher, D.: Efficient graph-based image segmentation. International Journal of Computer Vision 59, 167–181 (2004)CrossRefGoogle Scholar
  7. 7.
    Galun, M., Sharon, E., Basri, R., Brandt, A.: Texture segmentation by multiscale aggregation of filter responses and shape elements. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, vol. 1, pp. 716–723. IEEE (2003)Google Scholar
  8. 8.
    Vu, H., Olsson, R.: Automatic Improvement of Graph Based Image Segmentation. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Fowlkes, C., Wang, S., Choi, M.-H., Mantler, S., Schulze, J., Acevedo, D., Mueller, K., Papka, M. (eds.) ISVC 2012, Part II. LNCS, vol. 7432, pp. 578–587. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Løkketangen, A., Olsson, R.: Generating meta-heuristic optimization code using adate. Journal of Heuristics 16, 911–930 (2010)Google Scholar
  10. 10.
    Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and texture analysis for image segmentation. International Journal of Computer Vision 43(1), 7–27 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Milner, R., Tofte, M., Harper, R., MacQueen, D.: The Definition of Standard ML - Revised. The MIT Press (1997)Google Scholar
  12. 12.
    Olsson, R.: Inductive functional programming using incremental program tranformation. Artificial Intelligence 74, 55–81 (1995)CrossRefGoogle Scholar
  13. 13.
    Rijsbergen, C.J.V.: Information Retrieval, 2nd edn. Butterworth-Heinemann, Newton (1979)Google Scholar
  14. 14.
    Sharon, E., Galun, M., Sharon, D., Basri, R., Brandt, A.: Hierarchy and adaptivity in segmenting visual scenes. Nature 442(7104), 810–813 (2006)CrossRefGoogle Scholar
  15. 15.
    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)CrossRefGoogle Scholar
  16. 16.
    Tarjan, R.E.: Efficiency of a good but not linear set union algorithm. Journal of the ACM (JACM) 22(2), 215–225 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1101–1113 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.IT DepartmentØstfold University CollegeHaldenNorway

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