Advertisement

Adaptive selection of image classifiers

  • Giorgio Giacinto
  • Fabio Roli
Session 2: Image Analysis & Pattern Recognition
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

Recently, the concept of “Multiple Classifier Systems” was proposed as a new approach to the development of high performance image classification systems. Multiple Classifier Systems can be used to improve classification accuracy by combining the outputs of classifiers making “uncorrelated” errors. Unfortunately, in real image recognition problems, it may be very difficult to design an ensemble of classifiers that satisfies this assumption. In this paper, we propose a different approach based on the concept of “adaptive selection” of multiple classifiers in order to select the most appropriate classifier for each input pattern. We point out that adaptive selection does not require the assumption of uncorrelated errors, thus simplifying the choice of classifiers forming a Multiple Classifier System. Reported results on the classification of remote-sensing images show that adaptive selection can be used to obtain substantial improvements in classification accuracy.

Keywords

Classification Accuracy Test Pattern Input Pattern Select Condition Adaptive Selection 
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.
    L.Xu, A.Krzyzak, and C.Y.Suen, “Methods for combining multiple classifiers and their applications to handwriting recognition”, IEEE Trans. on Systems, Man, and Cyb., Vol. 22, No. 3, May/June 1992, pp. 418–435Google Scholar
  2. 2.
    R.Battiti, and A.M.Colla, “Democracy in neural nets: voting schemes for classification”, Neural Networks, Vol. 7, No. 4, 1994, pp. 691–707Google Scholar
  3. 3.
    F.Roli, G.Giacinto, and G.Vernazza, “Comparison and combination of statistical and neural network algorithms for remote-sensing image classification”, Neurocomputation in Remote Sensing Data Analysis, Advances in Spatial Science Series, Springer Verlag Ed. (in press, 1997)Google Scholar
  4. 4.
    G.Giacinto, and F.Roli, “Ensembles of Neural Networks for Soft Classification of Remote Sensing Images”, Proc. of the European Symposium on Intelligent Techniques, March 20–21, 1997, Bari, Italy, pp.166–170Google Scholar
  5. 5.
    I.Kanellopoulos et al., “Integration of neural network and statistical image classification for land cover mapping”, Proc. IGARSS 93, Tokio, 18–21 August 1993, pp. 511–513Google Scholar
  6. 6.
    Y.S.Huang, and C.Y.Suen, “A method of combining multiple experts for the recognition of unconstrained handwritten numerals”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.17, No.1, January 1995, pp.90–94Google Scholar
  7. 7.
    N.Srihari et al., “Decision combination in multiple classifier systems”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.16, No.1, Jan. 1994, pp. 66–75Google Scholar
  8. 8.
    L.K.Hansen, and P.Salamon, “Neural network ensembles”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 12, No. 10, October 1990, pp. 993–1001Google Scholar
  9. 9.
    K.Tumer and J.Gosh, “Error correlation and error reduction in ensemble classifiers”, Tech. Report, Dept. of ECE, University of Texas, July 11, 1996Google Scholar
  10. 10.
    D.Partridge, W.B.Yates, “Engineering multiversion neural-net systems”, Neural Computation, 8, 1996, pp. 869–893Google Scholar
  11. 11.
    R.Jacobs, M.Jorclan, S.Nowlan, and G.Hinton, “Adaptive mixtures of local experts”, Neural Computation, 3, 1991, pp. 79–87Google Scholar
  12. 12.
    C.Y.Suen et al., “The combination of multiple classifiers by a neural network approach”, Int. Journal of Pattern Recognition and Artificial Intelligence, Vol. 9, no.3, 1995, pp.579–597Google Scholar
  13. 13.
    D.Opitz, and J.Shavlik, “Generating accurate and diverse members of a neural-network ensemble”, Advances in Neural Information Processing Systems 8, MIT Press, 1996Google Scholar
  14. 14.
    R.O.Duda, P.E.Hart, “Pattern Classification and Scene Analysis”, Wiley & Sons, Inc., 1973Google Scholar
  15. 15.
    S.B.Serpico, L.Bruzzone and F.Roli, “An experimental comparison of neural and statistical non-parametric algorithms for supervised classification of remote-sensing images”, Pattern Recognition Letters, Vol 17, No. 13, November 1996, pp. 1331–1341Google Scholar
  16. 16.
    F.Roli and G.Giacinto, “Adaptive selection in multiple classifier systems”, Tech. Rep., MCS-4-96, University of Cagliari, Italy, 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Giorgio Giacinto
    • 1
  • Fabio Roli
    • 1
  1. 1.Dept. of Electrical and Electronic Eng.University of Cagliari, ItalyCagliariItaly

Personalised recommendations