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Learning High-Level Visual Concepts Using Attributed Primitives and Genetic Programming

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Applications of Evolutionary Computing (EvoWorkshops 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3907))

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Abstract

In this paper, we present a novel approach to genetic learning of high-level visual concepts that works with sets of attributed visual primitives rather than with raster images. The paper presents the approach in detail and verifies it in an experiment concerning locating objects in real-world 3D scenes.

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References

  1. Bhanu, B., Lin, Y., Krawiec, K.: Evolutionary Synthesis of Pattern Recognition Systems. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  2. Gabor, D.: Theory of Communication. J. Inst. of Electrical Engineers 93, 429–457 (1946)

    Google Scholar 

  3. 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. 373–390. MIT Press, Cambridge (1994)

    Google Scholar 

  4. Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.A.: Genetic Programming III: Darwinian Invention and Problem Solving. Morgan Kaufman, San Francisco (1999)

    MATH  Google Scholar 

  5. Krawiec, K., Bhanu, B.: Visual Learning by Coevolutionary Feature Synthesis. IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 35, 409–425 (2005)

    Article  Google Scholar 

  6. Luke, S.: ECJ Evolutionary Computation System (2002), http://www.cs.umd.edu/projects/plus/ec/ecj/

  7. Maloof, M.A., Langley, P., Binford, T.O., Nevatia, R., Sage, S.: Improved rooftop detection in aerial images with machine learning. Machine Learning 53, 157–191 (2003)

    Article  Google Scholar 

  8. Marek, A., Smart, W.D., Martin, M.C.: Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming. In: Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO 2002), New York, pp. 330–336 (2002)

    Google Scholar 

  9. Marr, D.: Vision. W.H. Freeman, San Francisco (1982)

    Google Scholar 

  10. Rizki, M., Zmuda, M., Tamburino, L.: Evolving pattern recognition systems. IEEE Transactions on Evolutionary Computation 6, 594–609 (2002)

    Article  Google Scholar 

  11. Song, A., Ciesielski, V.: Fast texture segmentation using genetic programming. In: Sarker, R., et al. (eds.) Proceedings of the 2003 Congress on Evolutionary Computation CEC 2003, pp. 2126–2133. IEEE Press, Canberra (2003)

    Chapter  Google Scholar 

  12. Sun Microsystems Inc.: Java Advanced Imaging API Specification. Version 1.2 (2001)

    Google Scholar 

  13. Teller, A., Veloso, M.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 

  14. Torralba, A., Murphy, K.M., Freeman, W.T.: MIT-CSAIL Computer vision annotated image library (2004), http://web.mit.edu/torralba/www/database.html

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, Poznań, 60965, Poland

    Krzysztof Krawiec

Authors
  1. Krzysztof Krawiec
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Editor information

Editors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Franz Rothlauf

  2. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  3. Dipartimento di Ingegneria dell’Informazione, Università di Parma,  

    Stefano Cagnoni

  4. Centre of Informatics and Systems of the University of Coimbra,  

    Ernesto Costa

  5. Dept. LCC, Universidad de Málaga, Spain

    Carlos Cotta

  6. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  7. INRIA Saclay - Ile-de-France, Parc Orsay Université, 4, rue Jacques Monod, 91893, ORSAY Cedex, France

    Evelyne Lutton

  8. CISUC, Department of Informatics Engineering, University of Coimbra, Polo II of the University of Coimbra, 3030, Coimbra, Portugal

    Penousal Machado

  9. Dartmouth College, Lebanon, NH, USA

    Jason H. Moore

  10. Universidade de A Coruña, CP 15071, A Coruña, Spain

    Juan Romero

  11. School of Computing Sciences, UEA Norwich, University of East Anglia, NR4 7TJ, Norwich, UK

    George D. Smith

  12. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  13. Kyushu University, Japan

    Hideyuki Takagi

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© 2006 Springer-Verlag Berlin Heidelberg

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Krawiec, K. (2006). Learning High-Level Visual Concepts Using Attributed Primitives and Genetic Programming. In: Rothlauf, F., et al. Applications of Evolutionary Computing. EvoWorkshops 2006. Lecture Notes in Computer Science, vol 3907. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732242_48

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  • DOI: https://doi.org/10.1007/11732242_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33237-4

  • Online ISBN: 978-3-540-33238-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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