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A New Wavelet-Based Texture Descriptor for Image Retrieval

  • Esther de Ves
  • Ana Ruedin
  • Daniel Acevedo
  • Xaro Benavent
  • Leticia Seijas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4673)

Abstract

This paper presents a novel texture descriptor based on the wavelet transform. First, we will consider vertical and horizontal coefficients at the same position as the components of a bivariate random vector. The magnitud and angle of these vectors are computed and its histograms are analyzed. This empirical magnitud histogram is modelled by using a gamma distribution (pdf). As a result, the feature extraction step consists of estimating the gamma parameters using the maxima likelihood estimator and computing the circular histograms of angles. The similarity measurement step is done by means of the well-known Kullback-Leibler divergence. Finally, retrieval experiments are done using the Brodatz texture collection obtaining a good performance of this new texture descriptor. We compare two wavelet transforms, with and without downsampling, and show the advantage of the second one, which is translation invariant, for the construction of our texture descriptor.

Keywords

Texture descriptor Wavelet Transform Image retrieval 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Esther de Ves
    • 1
  • Ana Ruedin
    • 2
  • Daniel Acevedo
    • 2
  • Xaro Benavent
    • 3
  • Leticia Seijas
    • 2
  1. 1.Computer Science Department, University of Valencia 
  2. 2.Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires 
  3. 3.Robotics Institute, University of Valencia 

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