Title
On the geometry of multivariate generalized Gaussian models On the geometry of multivariate generalized Gaussian models
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Beijing ,
Subject
Mathematics
Physics
Computer. Automation
Source (journal)
Journal of mathematical imaging and vision. - Beijing
Volume/pages
43(2012) :3 , p. 180-193
ISSN
0924-9907
ISI
000302346300002
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
This paper concerns the geometry of the zero-mean multivariate generalized Gaussian distribution (MGGD) and the calculation of geodesic distances on the MGGD manifold. The MGGD is a suitable distribution for the modeling of multivariate (color, multispectral, vector and tensor images, etc.) image wavelet statistics. Expressions are derived for the Fisher-Rao metric for the zero-mean MGGD model. A closed-form expression is obtained for the geodesic distance on the submanifolds characterized by a fixed MGGD shape parameter. Suitable approximate solutions to the geodesic equations are presented in the case of MGGDs with varying shape parameters. An application to image texture similarity measurement in the wavelet domain is briefly discussed, comparing the performance of the geodesic distance and the Kullback-Leibler divergence.
E-info
https://repository.uantwerpen.be/docman/iruaauth/7e126a/bada4cd3f62.pdf
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