Joint linear-circular stochastic models for texture classification
Marie-Cécile Peron, Jean‐Pierre da Costa, Youssef Stitou, Christian Germain, Yannick Berthoumieu · 2009
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 psi- 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 L1and Kullback-Leibler distances.