On Improving Dissimilarity-Based Classifications Using a Statistical Similarity Measure

  • Sang-Woon Kim
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

The aim of this paper is to present a dissimilarity measure strategy by which a new philosophy for pattern classification pertaining to dissimilarity-based classifications (DBCs) can be efficiently implemented. In DBCs, classifiers are not based on the feature measurements of individual patterns, but rather on a suitable dissimilarity measure among the patterns. In image classification tasks, such as face recognition, one of the most intractable problems is the distortion and lack of information caused by the differences in illumination and insufficient data. To overcome the above problem, in this paper, we study a new way of measuring the dissimilarity distance between two images of an object using a statistical similarity metric, which is measured based on intra-class statistics of data and does not suffer from the insufficient number of the data. Our experimental results, obtained with well-known benchmark databases, demonstrate that when the dimensionality of the dissimilarity representation has been appropriately chosen, DBCs can be improved in terms of classification accuracies.

Keywords

Face Recognition Dissimilarity Measure Face Database Dissimilarity Matrix Benchmark Database 
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.
    Adini, Y., Moses, Y., Ullman, S.: Face recognition: the problem of compensating for changes in illumination direction. IEEE Trans. Pattern Anal. and Machine Intell. 19(7), 721–732 (1997)CrossRefGoogle Scholar
  2. 2.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine, CA (2007), Can also be downloaded as of http://www.ics.uci.edu/~mlearn/MLRepository.html (February 2010)Google Scholar
  3. 3.
    Borg, I., Groenen, P.: Morden Mutlidimensional Scaling: Theory and Applications. Springer, New York (1997)CrossRefMATHGoogle Scholar
  4. 4.
    Duin, R.P.W., Pekalska, E., Harol, A., Lee, W.-J., Bunke, H.: On Euclidean corrections for non-Euclidean dissimilarities. In: da Vitoria Lobo, N., Kasparis, T., Roli, F., Kwok, J.T., Georgiopoulos, M., Anagnostopoulos, G.C., Loog, M. (eds.) SS+SSPR 2008. LNCS, vol. 5342, pp. 664–673. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Fan, R.-E., Chen, P.-H., Lin, C.-J.: Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889–1918 (2005)MATHGoogle Scholar
  6. 6.
    Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. and Machine Intell. 23(6), 643–660 (2001)CrossRefGoogle Scholar
  7. 7.
    Hu, Y., Wang, Z.: A similarity measure based on Hausdorff distance for human face recognition. In: Proceedings of 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, vol. 3, pp. 1131–1134 (2006)Google Scholar
  8. 8.
    Kim, S.-W., Gao, J.: On using dimensionality reduction schemes to optimize dissimilarity-based classifiers. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 309–316. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Kim, S.-W., Duin, R.P.W.: On optimizing dissimilarity-based classifier using multi-level fusion strategies. Journal of The Institute of Electronics Engineers of Korea 45-CI(5), 15–24 (2008) (in Korean); A preliminary version of this paper was presented at Kobti, Z., Wu, D. (eds.): Canadian AI 2007. LNCS (LNAI), vol. 4509, pp. 110–121. Springer, Heidelberg (2007)Google Scholar
  10. 10.
    Lee, K., Park, H.: A new similarity measure based on intraclass statistics for biometric systems. ETRI Journal 25(5), 401–406 (2003)CrossRefGoogle Scholar
  11. 11.
    Pekalska, E., Duin, R.P.W.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications. World Scientific Publishing, Singapore (2005)CrossRefMATHGoogle Scholar
  12. 12.
    Riesen, K., Kilchherr, V., Bunke, H.: Reducing the dimensionality of vector space embeddings of graphs. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 563–573. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Samaria, F., Harter, A.: Parameterisation of a stochastic model for human face identification. In: Proceedings of 2nd IEEE Workshop on Applications of Computer Vision, Sarasota FL, pp. 215–220 (1994)Google Scholar
  14. 14.
    Sim, T., Baker, S., Bsat, M.: The CMU pose, illumination, and expression(PIE) database of human faces, Technical report CMU-RI-TR-01-02, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (2001)Google Scholar
  15. 15.
    Woznica, A., Kalousis, A., Hilario, M.: Learning to combine distances for complex representations. In: Proceedings of the 24th International Conference on Machine Learning, Corvallis OR, pp. 1031–1038 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sang-Woon Kim
    • 1
  • Robert P. W. Duin
    • 2
  1. 1.Dept. of Computer Science and EngineeringMyongji UniversityYonginSouth Korea
  2. 2.Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyThe Netherlands

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