Title
Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models Multiscale colour texture retrieval using the geodesic distance between multivariate generalized Gaussian models
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
conferenceObject
Publication
S.l. , [*]
Subject
Physics
Engineering sciences. Technology
Source (book)
IEEE International Conference on Image Processing, San Diego, Calif., October 12-15, 2008
ISSN
1522-4880
ISBN
978-1-4244-1765-0
ISI
000262505000043
Carrier
E
Target language
English (eng)
Affiliation
University of Antwerp
Abstract
This contribution concerns the retrieval of colour texture. The interband correlation structure is considered by modeling the heavy-tailed image wavelet histograms with a multivariate generalized Gaussian. As a similarity measure we propose to use the Rao geodesic distance, which; in contrast to the Kullback-Leibler divergence, exists in a closed form for any fixed value of the shape parameter of the distribution. We apply this in several retrieval experiments. The modeling of the interband correlation significantly increases retrieval rates, while the geodesic distance is shown to outperform the Kullback-Leibler divergence. A multivariate Laplace distribution yields better results than a Gaussian, indicating the potential of a model with variable shape parameter together with the geodesic distance.
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