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This paper introduces an intrinsic prior distribution for supervised classification of texture images. First, we introduce the intrinsic prior distribution as the normal law on a Riemannian manifold. Next, based on this definition, we derive the estimation and classification schemes. Finally, we propose an application for the classification of texture images. Experiments on the VisTex texture database...
This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on multivariate stochastic modeling. The main interest of the multivariate modeling comparatively to the univariate case is to consider spatial dependency as additional features for characterizing...
Natural texture images exhibit a high intra-class diversity due to different acquisition conditions (scene enlightenment, perspective angle, … ). To handle with the diversity, a new supervised classification algorithm based on a parametric formalism is introduced: the K-centroids-based classifier (K-CB). A comparative study between various supervised classification algorithms on the VisTex and Brodatz...
This paper introduces a centroid-based (CB) supervised classification algorithm of textured images. In the context of scale/orientation decomposition, we demonstrate the possibility to develop centroid approach based on a stochastic modeling. The aim of this paper is twofold. Firstly, we introduce the generalized Gamma distribution (GΓD) for the modeling of wavelet coefficients. A comparative goodness-of-fit...
The goal of the paper1 is to propose a new method for texture clustering based on the information-geometry tools. Considering textured images as a collection of heavy-tailed prior probability distributions related to some space/scale decomposition, an average of distributions, i.e. a barycentric distribution, is proposed for characterizing each cluster. We suggest the use of the Jeffrey divergence...
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