Skip to main content
Springer Nature Link
Log in

An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval

  • Conference paper
Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

Included in the following conference series:

  • 3837 Accesses

Abstract

In this paper we propose a new distance metric for probability density functions (PDF). The main advantage of this metric is that unlike the popular Kullback-Liebler (KL) divergence it can be computed in closed form when the PDFs are modeled as Gaussian Mixtures (GM). The application in mind for this metric is histogram based image retrieval. We experimentally show that in an image retrieval scenario the proposed metric provides as good results as the KL divergence at a fraction of the computational cost. This metric is also compared to a Bhattacharyya-based distance metric that can be computed in closed form for GMs and is found to produce better results.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. McLachlan, G., Peel, D.: Finite Mixture Models. Wiley, Chichester (2000)

    Book  MATH  Google Scholar 

  2. Han, J., Ma, K.: Fuzzy Color Histogram and its use in Color Image Retrieval. IEEE Trans. on Image Processing 11(8) (August 2003)

    Google Scholar 

  3. Del Bimbo, A.: Visual information Retrieval. Morgan Kaufmann publishers, San Francisco (1999)

    Google Scholar 

  4. Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, London (1990)

    MATH  Google Scholar 

  5. Ray, S.: Distance-Based Model Selection with Application to the Analysis of Gene Expression Data. Ms Thesis, Dept. of Statistics, Pennsylvania State University (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Ioannina, Ioannina, GR 45110, Greece

    G. Sfikas, C. Constantinopoulos, A. Likas & N. P. Galatsanos

Authors
  1. G. Sfikas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Constantinopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Likas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. P. Galatsanos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sfikas, G., Constantinopoulos, C., Likas, A., Galatsanos, N.P. (2005). An Analytic Distance Metric for Gaussian Mixture Models with Application in Image Retrieval. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_132

Download citation

  • DOI: https://doi.org/10.1007/11550907_132

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics