NDLI: Multivariate texture discrimination using a principal geodesic classifier

Content Provider	IEEE Xplore Digital Library
Author	Shabbir, A. Verdoolaege, G.
Copyright Year	2015
Description	Author affiliation: Dept. of Appl. Phys., Ghent Univ., Ghent, Belgium (Shabbir, A.; Verdoolaege, G.)
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.
Starting Page	3550
Ending Page	3554
File Size	1339990
Page Count	5
File Format	PDF
e-ISBN	9781479983391
DOI	10.1109/ICIP.2015.7351465
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Manifolds Level measurement Image color analysis Principal component analysis Probability distribution Databases Euclidean distance geodesic distance Texture classification principal geodesic analysis
Content Type	Text
Resource Type	Article

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