Advertisement

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

  • Wojciech Jaśkowski
  • Krzysztof Krawiec
  • Bartosz Wieloch
Part of the Studies in Computational Intelligence book series (SCI, volume 213)

Abstract

We propose a novel method of evolutionary visual learning that uses a generative approach to assess the learner’s ability to recognize image contents. Each learner, implemented as a genetic programming (GP) individual, processes visual primitives that represent local salient features derived from the input image. The learner analyzes the visual primitives, which involves mostly their grouping and selection, eventually producing a hierarchy of visual primitives build upon the input image. Based on that it provides partial reproduction of the shapes of the analyzed objects 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. In particular, we show how GP individuals trained on examples from different decision classes can be combined to build a complete multiclass recognition system. We compare such recognition systems to reference methods, showing that our generative learning approach provides similar results. This chapter also contains detailed analysis of processing carried out by an exemplary individual.

Keywords

Genetic Programming Input Image Recognition System Synthetic Aperture Radar Generative Learning 
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.
    Bhanu, B., Lin, Y., Krawiec, K.: Evolutionary Synthesis of Pattern Recognition Systems. Springer, 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(3), 409–425 (2005)CrossRefGoogle Scholar
  3. 3.
    Koza, J.R.: Genetic programming – 2. MIT Press, Cambridge (1994)Google Scholar
  4. 4.
    Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D.: Genetic Programming – An Introduction; On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco (1998)zbMATHGoogle Scholar
  5. 5.
    Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  6. 6.
    Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer, Berlin (1994)zbMATHGoogle Scholar
  7. 7.
    Koza, J.R.: Human-competitive applications of genetic programming. In: Ghosh, A., Tsutsui, S. (eds.) Advances in Evolutionary Computing: Theory and Applications, pp. 663–682. Springer, Berlin (2003)Google Scholar
  8. 8.
    Tackett, W.A.: Genetic programming for feature discovery and image discrimination. In: Forrest, S. (ed.) Proceedings of the 5th International Conference on Genetic Algorithms, ICGA 1993, University of Illinois at Urbana-Champaign, pp. 303–309. Morgan Kaufmann, San Francisco (1993)Google Scholar
  9. 9.
    Johnson, M.P., Maes, P., Darrell, T.: Evolving visual routines. In: Brooks, R.A., Maes, P. (eds.) ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems, pp. 198–209. MIT, Cambridge (1994)Google Scholar
  10. 10.
    Daida, J.M., Bersano-Begey, T.F., Ross, S.J., Vesecky, J.F.: Computer-assisted design of image classification algorithms: Dynamic and static fitness evaluations in a scaffolded genetic programming environment. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 279–284. MIT Press, Cambridge (1996)Google Scholar
  11. 11.
    Winkeler, J.F., Manjunath, B.S.: Genetic programming for object detection. In: Koza, J.R., Deb, K., Dorigo, M., Fogel, D.B., Garzon, M., Iba, H., Riolo, R.L. (eds.) Genetic Programming 1997: Proceedings of the Second Annual Conference, Stanford University, CA, USA, pp. 330–335. Morgan Kaufmann, San Francisco (1997)Google Scholar
  12. 12.
    Rizki, M.M., Zmuda, M.A., Tamburino, L.A.: Evolving pattern recognition systems. IEEE Transactions on Evolutionary Computation 6(6), 594–609 (2002)CrossRefGoogle Scholar
  13. 13.
    Howard, D., Roberts, S.C., Brankin, R.: Evolution of ship detectors for satellite SAR imagery. In: Langdon, W.B., Fogarty, T.C., Nordin, P., Poli, R. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 135–148. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  14. 14.
    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, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Teller, A., Veloso, M.: PADO: A new learning architecture for object recognition. In: Ikeuchi, K., Veloso, M. (eds.) Symbolic Visual Learning, pp. 81–116. Oxford University Press, Oxford (1996)Google Scholar
  16. 16.
    Poli, R.: Genetic programming for image analysis. In: Koza, J.R., Goldberg, D.E., Fogel, D.B., Riolo, R.L. (eds.) Genetic Programming 1996: Proceedings of the First Annual Conference, Stanford University, CA, USA, pp. 363–368. MIT Press, Cambridge (1996)Google Scholar
  17. 17.
    Howard, D., Roberts, S.C.: Evolving object detectors for infrared imagery: a comparison of texture analysis against simple statistics. In: Miettinen, K., Makela, M.M., Neittaanmaki, P., Periaux, J. (eds.) Evolutionary Algorithms in Engineering and Computer Science, Jyvaskyla, Finland, pp. 79–86. John Wiley & Sons, Chichester (1999)Google Scholar
  18. 18.
    Lett, M., Zhang, M.: New fitness functions in genetic programming for object detection. In: Pairman, D., North, H., McNeill, S. (eds.) Proceeding of Image and Vision Computing International Conference, Akaroa, New Zealand, Lincoln, Landcare Research, pp. 441–446 (2004)Google Scholar
  19. 19.
    Krawiec, K.: Pairwise comparison of hypotheses in evolutionary learning. In: Brodley, C., Pohoreckyj-Danyluk, A. (eds.) Proc. Eighteenth International Conference on Machine Learning, pp. 266–273. Morgan Kaufmann, San Francisco (2001)Google Scholar
  20. 20.
    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, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    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(6), 592–606 (1996)CrossRefGoogle Scholar
  22. 22.
    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 (IWFHR 2004), pp. 20–25. IEEE Computer Society, Washington (2004)CrossRefGoogle Scholar
  23. 23.
    Langley, P.: Elements of machine learning. Morgan Kaufmann, San Francisco (1996)Google Scholar
  24. 24.
    Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence Journal 2, 273–324 (1997)CrossRefGoogle Scholar
  25. 25.
    Jaśkowski, W.: Genetic programming with cross-task knowledge sharing for learning of visual concepts. Master’s thesis, Poznan University of Technology, Poznań, Poland (2006), http://www.cs.put.poznan.pl/wjaskowski/pub/papers/jaskowski06crosstask.pdf
  26. 26.
    Wieloch, B.: Genetic programming with knowledge modularization for learning of visual concepts. Master’s thesis, Poznan University of Technology, Poznań, Poland (2006)Google Scholar
  27. 27.
    Krawiec, K.: Generative learning of visual concepts using multiobjective genetic programming. Pattern Recognition Letters 28(16), 2385–2400 (2007)CrossRefGoogle Scholar
  28. 28.
    Luke, S.: ECJ evolutionary computation system (2002), http://cs.gmu.edu/eclab/projects/ecj/
  29. 29.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Wojciech Jaśkowski
    • 1
  • Krzysztof Krawiec
    • 1
  • Bartosz Wieloch
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznańPoland

Personalised recommendations