TY - GEN
T1 - Principal geodesic distance on a multivariate generalized gaussian manifold
AU - Verdoolaege, Geert
AU - Shabbir, Aqsa
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We present a new texture discrimination method for textured color images in the wavelet domain. In each wavelet subband, the correlation between the color bands is modeled by a multivariate generalized Gaussian distribution with fixed shape parameter (Gaussian, Laplacian). On the corresponding Riemannian manifold, the shape of texture clusters is characterized by means of principal geodesic analysis, specifically by the principal geodesic along which the cluster exhibits its largest variance. Then, the similarity of a texture to a class is defined in terms of the Rao geodesic distance on the manifold from the texture’s distribution to its projection on the principal geodesic of that class. This similarity measure is used in a classification scheme, referred to as principal geodesic classification (PGC). It is shown to perform significantly better than several other classifiers.
AB - We present a new texture discrimination method for textured color images in the wavelet domain. In each wavelet subband, the correlation between the color bands is modeled by a multivariate generalized Gaussian distribution with fixed shape parameter (Gaussian, Laplacian). On the corresponding Riemannian manifold, the shape of texture clusters is characterized by means of principal geodesic analysis, specifically by the principal geodesic along which the cluster exhibits its largest variance. Then, the similarity of a texture to a class is defined in terms of the Rao geodesic distance on the manifold from the texture’s distribution to its projection on the principal geodesic of that class. This similarity measure is used in a classification scheme, referred to as principal geodesic classification (PGC). It is shown to perform significantly better than several other classifiers.
KW - Principal geodesic analysis
KW - Rao geodesic distance
KW - Texture classification
UR - https://www.scopus.com/pages/publications/84950327504
U2 - 10.1007/978-3-319-25040-3_41
DO - 10.1007/978-3-319-25040-3_41
M3 - Conference contribution
AN - SCOPUS:84950327504
SN - 9783319250397
SN - 9783319250397
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 379
EP - 386
BT - Geometric Science of Information - 2nd International Conference, GSI 2015, Proceedings
A2 - Nielsen, Frank
A2 - Nielsen, Frank
A2 - Nielsen, Frank
A2 - Barbaresco, Frederic
A2 - Barbaresco, Frederic
A2 - Nielsen, Frank
PB - Springer
T2 - 2nd International Conference on Geometric Science of Information, GSI 2015
Y2 - 28 October 2015 through 30 October 2015
ER -