A new wavelet-based texture descriptor for image retrieval

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...

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Detalles Bibliográficos
Autores principales:	De Ves, E., Ruedin, A., Acevedo, D., Benavent, X., Seijas, L.
Formato:	SER
Materias:	Image retrieval Texture descriptor Wavelet transform Kullback-Leibler divergence Feature extraction Image analysis Vectors Wavelet transforms
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_03029743_v4673LNCS_n_p895_DeVes
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	todo:paper_03029743_v4673LNCS_n_p895_DeVes
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spelling	todo:paper_03029743_v4673LNCS_n_p895_DeVes2023-10-03T15:18:59Z A new wavelet-based texture descriptor for image retrieval De Ves, E. Ruedin, A. Acevedo, D. Benavent, X. Seijas, L. Image retrieval Texture descriptor Wavelet transform Kullback-Leibler divergence Texture descriptor Feature extraction Image analysis Image retrieval Vectors Wavelet transforms 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. © Springer-Verlag Berlin Heidelberg 2007. Fil:Ruedin, A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Acevedo, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Seijas, L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_03029743_v4673LNCS_n_p895_DeVes
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Image retrieval Texture descriptor Wavelet transform Kullback-Leibler divergence Texture descriptor Feature extraction Image analysis Image retrieval Vectors Wavelet transforms
spellingShingle	Image retrieval Texture descriptor Wavelet transform Kullback-Leibler divergence Texture descriptor Feature extraction Image analysis Image retrieval Vectors Wavelet transforms De Ves, E. Ruedin, A. Acevedo, D. Benavent, X. Seijas, L. A new wavelet-based texture descriptor for image retrieval
topic_facet	Image retrieval Texture descriptor Wavelet transform Kullback-Leibler divergence Texture descriptor Feature extraction Image analysis Image retrieval Vectors Wavelet transforms
description	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. © Springer-Verlag Berlin Heidelberg 2007.
format	SER
author	De Ves, E. Ruedin, A. Acevedo, D. Benavent, X. Seijas, L.
author_facet	De Ves, E. Ruedin, A. Acevedo, D. Benavent, X. Seijas, L.
author_sort	De Ves, E.
title	A new wavelet-based texture descriptor for image retrieval
title_short	A new wavelet-based texture descriptor for image retrieval
title_full	A new wavelet-based texture descriptor for image retrieval
title_fullStr	A new wavelet-based texture descriptor for image retrieval
title_full_unstemmed	A new wavelet-based texture descriptor for image retrieval
title_sort	new wavelet-based texture descriptor for image retrieval
url	http://hdl.handle.net/20.500.12110/paper_03029743_v4673LNCS_n_p895_DeVes
work_keys_str_mv	AT devese anewwaveletbasedtexturedescriptorforimageretrieval AT ruedina anewwaveletbasedtexturedescriptorforimageretrieval AT acevedod anewwaveletbasedtexturedescriptorforimageretrieval AT benaventx anewwaveletbasedtexturedescriptorforimageretrieval AT seijasl anewwaveletbasedtexturedescriptorforimageretrieval AT devese newwaveletbasedtexturedescriptorforimageretrieval AT ruedina newwaveletbasedtexturedescriptorforimageretrieval AT acevedod newwaveletbasedtexturedescriptorforimageretrieval AT benaventx newwaveletbasedtexturedescriptorforimageretrieval AT seijasl newwaveletbasedtexturedescriptorforimageretrieval
_version_	1782025923771498496

A new wavelet-based texture descriptor for image retrieval

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