Creative Commons Attribution-ShareAlike 4.0 International
This œuvre, Color Texture Discrimination using the Principal Geodesic Distance on a Multivariate Generalized Gau, by Geert Verdoolaege is licensed under a Creative Commons Attribution-ShareAlike 4.0 International license.

Color Texture Discrimination using the Principal Geodesic Distance on a Multivariate Generalized Gau


Color Texture Discrimination using the Principal Geodesic Distance on a Multivariate Generalized Gau
Publication details: 
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.
Source et DOI
Vidéo
Voir la vidéo
Color Texture Discrimination using the Principal Geodesic Distance on a Multivariate Generalized Gau
Groupes / audience: