Color Texture Discrimination using the Principal Geodesic Distance on a Multivariate Generalized Gau - Aqsa Shabbir, Geert Verdoolaege

[Pawamoy]

Author: Aqsa Shabbir, Geert Verdoolaege
DOI URL: http://dx.doi.org/10.1007/978-3-319-25040-3_41
Video: http://www.youtube.com/watch?v=rKBmPBPg0sU
Slides: https://drive.google.com/open?id=0B0QKxsVtOaTiUHc1X0d6SUM1eEk
Presentation: https://www.see.asso.fr/node/14303
Creative Commons Attribution-ShareAlike 4.0 International

Abstract:
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.