Combining SVM and Graph Matching in a Bayesian Multiple Classifier System for Image Content Recognition (original) (raw)

Abstract

In this paper, we propose an approach to image content recognition that exploits the benefits of different image representations to associate meaning with images. We choose classifiers based on global appearance, scene structure and region type occurrence, and define confidence measures on their output. The resulting posterior probabilities of the classifiers are combined in a Bayesian framework. We show that this method leads to a robust and efficient system that contributes to reducing the semantic gap between low level image features and higher level image descriptions.

Chapter PDF

Similar content being viewed by others

References

  1. Chapelle, O., Haffner, P., Vapnik, V.: Svms for histogram-based image classification. IEEE Trans. on Neural Networks 10, 1055–1065 (1999)
    Article Google Scholar
  2. Li, J., Wang, J.Z.: Automatic linguistic indexing of pictures by a statistic modeling approach. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 1075–1088 (2003)
    Article Google Scholar
  3. Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 264–271 (2003)
    Google Scholar
  4. Minka, T., Picard, R.: Interactive learning using a society of models. Pattern Recognition 30, 565–581 (1997)
    Article Google Scholar
  5. Beretti, S., Del Bimbo, A., Vicario, E.: Efficient matching and indexing of graph models in content-based retrieval. IEEE Trans. on Pattern Analysis and Machine Intelligence 23, 1089–1105 (2001)
    Article Google Scholar
  6. Le Saux, B., Amato, G.: Image recognition for digital libraries. In: ACM Multimedia/International Workshop on Multimedia Information Retrieval, pp. 91–98 (2004)
    Google Scholar
  7. Le Saux, B., Bunke, H.: Feature selection for graph-based image classifiers. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3523, pp. 147–154. Springer, Heidelberg (2005)
    Chapter Google Scholar
  8. Nilsson, N.J.: Principles of Artificial Intelligence. Tioga, Palo Alto (1980)
    MATH Google Scholar
  9. Kittler, J., Roli, F. (eds.): MCS 2000. LNCS, vol. 1857. Springer, Heidelberg (2000)
    Google Scholar
  10. Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 226–239 (1998)
    Article Google Scholar
  11. Tax, D.M., van Breukelen, M., Duin, R.P., Kittler, J.: Combining multiple classifiers by averaging or by multiplying? Pattern Recognition 33, 1475–1485 (2000)
    Article Google Scholar
  12. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
    MATH Google Scholar

Download references

Author information

Authors and Affiliations

  1. Institut für Informatik und Angewandte Mathematik, University of Bern, Neubrückstrasse, 10, CH-3012, Bern, Switzerland
    Bertrand Le Saux & Horst Bunke

Authors

  1. Bertrand Le Saux
  2. Horst Bunke

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Le Saux, B., Bunke, H. (2006). Combining SVM and Graph Matching in a Bayesian Multiple Classifier System for Image Content Recognition. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_76

Download citation

Publish with us