Title
Geodesics on the manifold of multivariate generalized Gaussian distributions with an application to multicomponent texture discrimination
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Dordrecht ,
Subject
Physics
Computer. Automation
Source (journal)
International journal of computer vision. - Dordrecht
Volume/pages
95(2011) :3 , p. 265-286
ISSN
0920-5691
ISI
000301842800003
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
We consider the Rao geodesic distance (GD) based on the Fisher information as a similarity measure on the manifold of zero-mean multivariate generalized Gaussian distributions (MGGD). The MGGD is shown to be an adequate model for the heavy-tailed wavelet statistics in multicomponent images, such as color or multispectral images. We discuss the estimation of MGGD parameters using various methods. We apply the GD between MGGDs to color texture discrimination in several classification experiments, taking into account the correlation structure between the spectral bands in the wavelet domain. We compare the performance, both in terms of texture discrimination capability and computational load, of the GD and the Kullback-Leibler divergence (KLD). Likewise, both uni- and multivariate generalized Gaussian models are evaluated, characterized by a fixed or a variable shape parameter. The modeling of the interband correlation significantly improves classification efficiency, while the GD is shown to consistently outperform the KLD as a similarity measure.
E-info
https://repository.uantwerpen.be/docman/iruaauth/df008c/5a377650b6d.pdf
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