DocumentCode :
3707874
Title :
Multivariate texture discrimination using a principal geodesic classifier
Author :
A. Shabbir;G. Verdoolaege
Author_Institution :
Department of Applied Physics, Ghent University, B-9000 Ghent, Belgium
fYear :
2015
Firstpage :
3550
Lastpage :
3554
Abstract :
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 texture 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 k-nearest 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.
Keywords :
"Manifolds","Level measurement","Image color analysis","Principal component analysis","Probability distribution","Databases","Euclidean distance"
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/ICIP.2015.7351465
Filename :
7351465
Link To Document :
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