Advertisement

Learning and Recognition of Hand-Drawn Shapes Using Generative Genetic Programming

  • Wojciech Jaśkowski
  • Krzysztof Krawiec
  • Bartosz Wieloch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4448)

Abstract

We describe a novel method of evolutionary visual learning that uses generative approach for assessing learner’s ability to recognize image contents. Each learner, implemented as a genetic programming individual, processes visual primitives that represent local salient features derived from a raw input raster image. In response to that input, the learner produces partial reproduction of the input image, and is evaluated according to the quality of that reproduction. We present the method in detail and verify it experimentally on the real-world task of recognition of hand-drawn shapes.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bhanu, B., Lin, Y., Krawiec, K.: Evolutionary Synthesis of Pattern Recognition Systems. Springer-Verlag, Berlin Heidelberg New York (2005)zbMATHGoogle Scholar
  2. 2.
    Krawiec, K., Bhanu, B.: Visual learning by coevolutionary feature synthesis. IEEE Transactions on System, Man, and Cybernetics – Part B. 35, 409–425 (2005)CrossRefGoogle Scholar
  3. 3.
    Krishnapuram, B., Bishop, C.M., Szummer, M.: Generative models and bayesian model comparison for shape recognition. In: IWFHR 2004. Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition, pp. 20–25. IEEE Computer Society, Washington, DC, USA (2004)Google Scholar
  4. 4.
    Koza, J.: Genetic programming – 2. MIT Press, Cambridge, MA (1994)zbMATHGoogle Scholar
  5. 5.
    Jaskowski, W.: Genetic programming with cross-task knowledge sharing for learning of visual concepts. Master’s thesis, Poznan University of Technology, Poznań, Poland (2006)Google Scholar
  6. 6.
    Wieloch, B.: Genetic programming with knowledge modularization for learning of visual concepts. Master’s thesis, Poznan University of Technology, Poznań, Poland (2006)Google Scholar
  7. 7.
    Teller, A., Veloso, M.: PADO: A new learning architecture for object recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 77–112. Oxford Press, New York (1997)Google Scholar
  8. 8.
    Rizki, M., Zmuda, M., Tamburino, L.: Evolving pattern recognition systems. IEEE Transactions on Evolutionary Computation 6, 594–609 (2002)CrossRefGoogle Scholar
  9. 9.
    Maloof, M., Langley, P., Binford, T., Nevatia, R., Sage, S.: Improved rooftop detection in aerial images with machine learning. Machine Learning 53, 157–191 (2003)CrossRefGoogle Scholar
  10. 10.
    Olague, G., Puente, C.: The honeybee search algorithm for three-dimensional reconstruction. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 427–437. Springer, Berlin Heidelberg New York (2006)CrossRefGoogle Scholar
  11. 11.
    Howard, D., Roberts, S.C., Ryan, C.: Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance. Pattern Recognition Letters 27, 1275–1288 (2006)CrossRefGoogle Scholar
  12. 12.
    Krawiec, K.: Learning high-level visual concepts using attributed primitives and genetic programming. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 515–519. Springer-Verlag, Berlin Heidelberg New York (2006)CrossRefGoogle Scholar
  13. 13.
    Krawiec, K.: Evolutionary learning of primitive-based visual concepts. In: Proc. IEEE Congress on Evolutionary Computation, Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, pp. 4451–4458 (July 16-21, 2006)Google Scholar
  14. 14.
    Revow, M., Williams, C.K.I., Hinton, G.E.: Using generative models for handwritten digit recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 18, 592–606 (1996)CrossRefGoogle Scholar
  15. 15.
    Luke, S.: ECJ evolutionary computation system (2002) (http://cs.gmu.edu/ eclab/projects/ecj/)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wojciech Jaśkowski
    • 1
  • Krzysztof Krawiec
    • 1
  • Bartosz Wieloch
    • 1
  1. 1.Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60965 PoznańPoland

Personalised recommendations