Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes
Inhaltsverzeichnis
Reference
Wojciech Jaskowski and Krzysztof Krawiec and Bartosz Wieloch: Genetic Programming for Generative Learning and Recognition of Hand-Drawn Shapes. Evolutionary Image Analysis and Signal Processing, Studies in Computational Intelligence, Vol. 213, pp. 73-90, Springer, 2009.
DOI
http://dx.doi.org/10.1007/978-3-642-01636-3_5
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.
Extended Abstract
Bibtex
Used References
Bhanu, B., Lin, Y., Krawiec, K.: Evolutionary Synthesis of Pattern Recognition Systems. Springer, New York (2005)
Krawiec, K., Bhanu, B.: Visual learning by coevolutionary feature synthesis. IEEE Transactions on System, Man, and Cybernetics – Part B 35(3), 409–425 (2005) http://dx.doi.org/10.1109/TSMCB.2005.846644
Koza, J.R.: Genetic programming – 2. MIT Press, Cambridge (1994)
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)
Goldberg, D.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)
Michalewicz, Z.: Genetic algorithms + data structures = evolution programs. Springer, Berlin (1994)
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)
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)
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)
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)
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)
Rizki, M.M., Zmuda, M.A., Tamburino, L.A.: Evolving pattern recognition systems. IEEE Transactions on Evolutionary Computation 6(6), 594–609 (2002) http://dx.doi.org/10.1109/TEVC.2002.806167
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) http://dx.doi.org/10.1007/3-540-48885-5_11
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) http://dx.doi.org/10.1007/11732242_38
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)
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)
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)
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)
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)
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) http://dx.doi.org/10.1007/11732242_48
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) http://dx.doi.org/10.1109/34.506410
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) http://dx.doi.org/10.1109/IWFHR.2004.46
Langley, P.: Elements of machine learning. Morgan Kaufmann, San Francisco (1996)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence Journal 2, 273–324 (1997) http://dx.doi.org/10.1016/S0004-3702(97)00043-X
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
Wieloch, B.: Genetic programming with knowledge modularization for learning of visual concepts. Master’s thesis, Poznan University of Technology, Poznań, Poland (2006)
Krawiec, K.: Generative learning of visual concepts using multiobjective genetic programming. Pattern Recognition Letters 28(16), 2385–2400 (2007) http://dx.doi.org/10.1016/j.patrec.2007.08.001
Luke, S.: ECJ evolutionary computation system (2002), http://cs.gmu.edu/eclab/projects/ecj/
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Links
Full Text
[extern file]
