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

  • G. Sfikas
  • C. Constantinopoulos
  • A. Likas
  • N. P. Galatsanos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)

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.

Keywords

Probability Density Function Probability Density Function Gaussian Mixture Model Image Retrieval Color Histogram 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • G. Sfikas
    • 1
  • C. Constantinopoulos
    • 1
  • A. Likas
    • 1
  • N. P. Galatsanos
    • 1
  1. 1.Department of Computer ScienceUniversity of IoanninaIoanninaGreece

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