An Efficient Distance Between Multi-dimensional Histograms for Comparing Images

  • Francesc Serratosa
  • Gerard Sanromà
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


The aim of this paper is to present an efficient distance between n-dimensional histograms. Some image classification or image retrieval techniques use the distance between histograms as a first step of the classification process. For this reason, some algorithms that find the distance between histograms have been proposed in the literature. Nevertheless, most of this research has been applied on one-dimensional histograms due to the computation of a distance between multi-dimensional histograms is very expensive. In this paper, we present an efficient method to compare multi-dimensional histograms in O(2z), where z represents the number of bins. Results show a huge reduction of the time consuming with no recognition-ratio reduction.


Computational Cost Distance Measure Image Retrieval Transportation Problem Huge Reduction 
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 2006

Authors and Affiliations

  • Francesc Serratosa
    • 1
  • Gerard Sanromà
    • 1
  1. 1.Dept. d’Enginyeria Informàtica i MatemàtiquesUniversitat Rovira i VirgiliSpain

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