Classification of High-Dimension PDFs Using the Hungarian Algorithm

  • James S. Cope
  • Paolo Remagnino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7626)

Abstract

The Hungarian algorithm can be used to calculate the earth mover’s distance, as a measure of the difference between two probability density functions, when the pdfs are described by sets of n points sampled from their distributions. However, information generated by the algorithm about precisely how the pdfs are different is not utilized. In this paper, a method is presented that incorporates this information into a ‘bag-of-words’ type method, in order to increase the robustness of a classification. This method is applied to an image classification problem, and is found to outperform several existing methods.

Keywords

Probability Density Function Feature Space Image Retrieval Transportation Problem Class Object 
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.
    Chen, X., Hu, X., Shen, X.: Spatial Weighting for Bag-of-Visual-Words and Its Application in Content-Based Image Retrieval. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 867–874. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Cope, J.S., Corney, D.P.A., Clark, J.Y., Remagnino, P., Wilkin, P.: Plant species identification using digital morphometrics: A reviews. Expert Systems with Applications 39, 7562–7573 (2012)CrossRefGoogle Scholar
  3. 3.
    Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Workshop on Statistical Learning in Computer Vision, ECCV, pp. 1–22 (2004)Google Scholar
  4. 4.
    Edmonds, J., Karp, R.M.: Theoretical improvements in algorithmic efficiency for network flow problems. Journal of the ACM 19, 248–264 (1972)MATHCrossRefGoogle Scholar
  5. 5.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Research Logistics Quarterly 2, 83–97 (1955)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Leung, T., Malik, J.: Representing and recognising the visual appearance of materials using three-dimensional textons. International Journal Of Computer Vision 43, 7–27 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Rubner, Y., Guibas, L.J., Tomasi, C.: The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval. In: ARPA Image Understanding Workshop, pp. 661–668 (1997)Google Scholar
  8. 8.
    Sivic, J., Zisserman, A.: Google video: A text retrieval approach to object matching in videos. In: International Conference On Computer Vision, vol. 2, pp. 1470–1477 (2003)Google Scholar
  9. 9.
    Sparck-Jones, K., Needham, R.M.: Automatic term classifications and retrieval. Information Storage And Retrieval 4, 91–100 (1968)CrossRefGoogle Scholar
  10. 10.
    Varma, M., Zisserman, A.: A statistical approach to material classification using image patch exemplars. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 2032–2047 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • James S. Cope
    • 1
  • Paolo Remagnino
    • 1
  1. 1.Digital Imaging Research CentreKingston UniversityLondonUK

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