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Wavelet-based colour texture retrieval using the kullback-leibler divergence between bivariate generalized gaussian models

Geert Verdoolaege UGent, Yves Rosseel UGent, Michiel Lambrechts and Paul Scheunders (2009) 2009 16TH IEEE International conference on image processing. 1-6. p.265-268
abstract
We study the retrieval of coloured textures from a database. In a statistical framework we model the heavy-tailed wavelet histograms through a generalized Gaussian distribution (GGD). We choose the Kullback-Leibler divergence (KLD) as a similarity measure and we obtain a closed-form expression for the KLD between two zero-mean bivariate GGDs. This allows us to take into account the rich correlation structure between the colour bands two by two. We show that this results in a considerably improved retrieval rate and, in addition, we demonstrate the superior performance of the bivariate GGD, in comparison with the bivariate Gaussian.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
in
2009 16TH IEEE International conference on image processing
volume
1-6
pages
4 pages
publisher
IEEE
place of publication
New York, NY, USA
conference name
16TH IEEE International Conference On Image Processing
conference location
Cairo, Egypt
conference start
2009-11-07
conference end
2009-11-10
Web of Science type
Proceedings Paper
Web of Science id
000280464300067
ISBN
9781424456536
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1149177
handle
http://hdl.handle.net/1854/LU-1149177
date created
2011-02-14 14:51:31
date last changed
2015-06-17 09:25:38
@inproceedings{1149177,
  abstract     = {We study the retrieval of coloured textures from a database. In a statistical framework we model the heavy-tailed wavelet histograms through a generalized Gaussian distribution (GGD). We choose the Kullback-Leibler divergence (KLD) as a similarity measure and we obtain a closed-form expression for the KLD between two zero-mean bivariate GGDs. This allows us to take into account the rich correlation structure between the colour bands two by two. We show that this results in a considerably improved retrieval rate and, in addition, we demonstrate the superior performance of the bivariate GGD, in comparison with the bivariate Gaussian.},
  author       = {Verdoolaege, Geert and Rosseel, Yves and Lambrechts, Michiel and Scheunders, Paul},
  booktitle    = {2009 16TH IEEE International conference on image processing},
  isbn         = {9781424456536},
  language     = {eng},
  location     = {Cairo, Egypt},
  pages        = {265--268},
  publisher    = {IEEE},
  title        = {Wavelet-based colour texture retrieval using the kullback-leibler divergence between bivariate generalized gaussian models},
  volume       = {1-6},
  year         = {2009},
}

Chicago
Verdoolaege, Geert, Yves Rosseel, Michiel Lambrechts, and Paul Scheunders. 2009. “Wavelet-based Colour Texture Retrieval Using the Kullback-leibler Divergence Between Bivariate Generalized Gaussian Models.” In 2009 16TH IEEE International Conference on Image Processing, 1-6:265–268. New York, NY, USA: IEEE.
APA
Verdoolaege, Geert, Rosseel, Y., Lambrechts, M., & Scheunders, P. (2009). Wavelet-based colour texture retrieval using the kullback-leibler divergence between bivariate generalized gaussian models. 2009 16TH IEEE International conference on image processing (Vol. 1–6, pp. 265–268). Presented at the 16TH IEEE International Conference On Image Processing, New York, NY, USA: IEEE.
Vancouver
1.
Verdoolaege G, Rosseel Y, Lambrechts M, Scheunders P. Wavelet-based colour texture retrieval using the kullback-leibler divergence between bivariate generalized gaussian models. 2009 16TH IEEE International conference on image processing. New York, NY, USA: IEEE; 2009. p. 265–8.
MLA
Verdoolaege, Geert, Yves Rosseel, Michiel Lambrechts, et al. “Wavelet-based Colour Texture Retrieval Using the Kullback-leibler Divergence Between Bivariate Generalized Gaussian Models.” 2009 16TH IEEE International Conference on Image Processing. Vol. 1–6. New York, NY, USA: IEEE, 2009. 265–268. Print.