Joint linear-circular stochastic models for texture classification

Abstract

In this paper, we investigate both linear and circular stochastic models in the context of texture discrimination. These models aim at representing the magnitudes and orientations obtained by a complex wavelet decomposition, such as the steerable pyramid.The novelty consists in considering specific parametric models for circular data such as von Mises and ψ-distributions to describe the distributions of orientations. Particular attention is paid to the choice of a metric and to its adequation to the models. Indexing experiments are conducted to quantitatively evaluate the performances of the proposed models and of the chosen matrices, i.e. the 1 L and Kullback-Leibler distances.

Copulas based multivariate gamma modeling for texture classification

“…Some recent works propose to use circular statistics, i.e. statistics of circular data [15] or joint linear-circular stochastic model [16] to characterize each subband coefficients. All these representation leads to a simple and attractive approach, defined by a limited set of parameters, but they do not provide a complete statistical description of the texture images.…”