Multivariate texture discrimination using a principal geodesic classifier

A. Shabbir, G. Verdoolaege
2015 2015 IEEE International Conference on Image Processing (ICIP)  
A new texture discrimination method is presented for classification and retrieval of colored textures represented in the wavelet domain. The interband correlation structure is modeled by multivariate probability models which constitute a Riemannian manifold. The presented method considers the shape of the class on the manifold by determining the principal geodesic of each class. The method, which we call principal geodesic classification, then determines the shortest distance from a test
more » ... to the principal geodesic of each class. We use the Rao geodesic distance (GD) for calculating distances on the manifold. We compare the performance of the proposed method with distance-to-centroid and knearest neighbor classifiers and of the GD with the Euclidean distance. The principal geodesic classifier coupled with the GD yields better results, indicating the usefulness of effectively and concisely quantifying the variability of the classes in the probabilistic feature space.
doi:10.1109/icip.2015.7351465 dblp:conf/icip/ShabbirV15 fatcat:7ull6tjmlzhz7doazhxzebwgxu