Simplifying Texture Classification

In texture classification modeling the full joint probability distribution of features is of questionable value. This paper demonstrates that marginal distributions of filter responses and marginal conditional distributions of intensity values over small neighborhoods are adequate to classify textures and can outperform methods using the joint distribution. The use of the Earth Mover’s Distance for marginal distributions is extended using PCA to build a Gaussian probability model for each class that captures the dependence between feature histograms. This framework is then generalized to include marginal conditional distributions for MRF models. These methods are demonstrated on the ColumbiaUtrecht database by classifying over 2800 images in all 61 texture classes. Results surpass those of Varma & Zisserman (CVPR ‘03) and Hayman (ECCV ’04).