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: Ruedin, Ana María Clara, Acevedo, Daniel G., Seijas, Leticia María
Publicado: 2007
Materias:
Image retrieval
Texture descriptor
Wavelet transform
Kullback-Leibler divergence
Feature extraction
Image analysis
Vectors
Wavelet transforms
Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4673LNCS_n_p895_DeVes
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 paper:paper_03029743_v4673LNCS_n_p895_DeVes
record_format dspace
spelling paper:paper_03029743_v4673LNCS_n_p895_DeVes2023-06-08T15:28:25Z A new wavelet-based texture descriptor for image retrieval Ruedin, Ana María Clara Acevedo, Daniel G. Seijas, Leticia María 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. 2007 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4673LNCS_n_p895_DeVes 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
Ruedin, Ana María Clara
Acevedo, Daniel G.
Seijas, Leticia María
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.
author Ruedin, Ana María Clara
Acevedo, Daniel G.
Seijas, Leticia María
author_facet Ruedin, Ana María Clara
Acevedo, Daniel G.
Seijas, Leticia María
author_sort Ruedin, Ana María Clara
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
publishDate 2007
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v4673LNCS_n_p895_DeVes
http://hdl.handle.net/20.500.12110/paper_03029743_v4673LNCS_n_p895_DeVes
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AT seijasleticiamaria anewwaveletbasedtexturedescriptorforimageretrieval
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