Advertisement

New Distance Measures Applied to Marble Classification

  • J. Caldas Pinto
  • J. M. Sousa
  • H. Alexandre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)

Abstract

Automatic marble classification based on their visual appearance is an important industrial issue, but due to the presence of randomly distributed high number of different colors and its subjective evaluation by human experts, the problem remains unsolved. In this paper, several new measures based on similarity tables built by human experts are introduced. They are used to improve the behavior of some clustering algorithms and to quantitatively characterize the results, increasing the correspondence of the measures to the visual appearance of the results. The obtained results show the effectiveness of the proposed methods.

Keywords

Genetic Algorithm Cluster Algorithm Simulated Annealing Similarity Score Fuzzy Cluster 
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.

References

  1. 1.
    Babuška, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston (1998)Google Scholar
  2. 2.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function. Plenum Press, New York (1981)zbMATHGoogle Scholar
  3. 3.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, New York (1995)Google Scholar
  4. 4.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice–Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  5. 5.
    Michalewicz, Z.: Genetic Algorithms + Data Strctures = Evolution Programs, 3rd edn. revised and extended. Springer, Berlin (1999)Google Scholar
  6. 6.
    Pinto, J.C., Marcolino, A., Ramalho, M.: Clustering algorithm for colour segmentation. In: Proc. of V Ibero-American Symposium On Pattern Recognition, SIARP 2000, Lisbon, Portugal, September 2000, pp. 611–617 (2000)Google Scholar
  7. 7.
    Pinto, J.C., Sousa, J.: Comparison of fuzzy clustering and quadtree methods applied to color segmentation. In: Proceedings of 12th Portuguese Conference on Pattern Recognition, RecPad 2002, pages Session: Pattern Recognition, Aveiro, Portugal, June 2002, pp. 1–4 (2002)Google Scholar
  8. 8.
    Salamon, P., Sibani, P., Frost, R.: Fact, Conjectures, and Improvements for Simulated Annealing. SIAM, Philadelphia (2002)CrossRefGoogle Scholar
  9. 9.
    Setnes, M., Roubos, H.: GA-fuzzy modeling and classification: complexity and performance. IEEE Transactions on Fuzzy Systems 8(5), 516–524 (2000)CrossRefGoogle Scholar
  10. 10.
    Sousa, J., Kaymak, U.: Fuzzy Decision Making in Modeling and Control. World Scientific Pub. Co., Singapore (2002)zbMATHCrossRefGoogle Scholar
  11. 11.
    Sousa, J.M., Kaymak, U., Madeira, S.: A comparative study of fuzzy target selection methods in direct marketing. In: Proceedings of 2002 IEEE World Congress on Computational Intelligence, WCCI 2002, FUZZ-IEEE 2002, pages Paper 1251, Hawaii, USA, May 2002, pp. 1–6 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • J. Caldas Pinto
    • 1
  • J. M. Sousa
    • 1
  • H. Alexandre
    • 1
  1. 1.Instituto Superior Técnico, Dept. of Mechanical Engineering, GCAR/IDMECTechnical University of LisbonLisbonPortugal

Personalised recommendations