Statistical Estimation of Histogram Variation for Texture Classification

I present a novel parametric approach for estimating the likelihood of homogeneously textured images. I propose that the dependence between pixel features is usefully captured by estimating the joint intra-class variation of their marginal distributions. To support this claim I build a single multivariate Gaussian distribution for each class that estimates the joint variation of several marginal, nonparametric, filter response histograms. I then generalize this framework to include marginal conditional distributions of pixel intensities for use with Strong-MRF models. I demonstrate these methods on the Columbia-Utrecht database by classifying over 2800 images in all 61 texture classes. In a direct comparison with Varma & Zisserman (ECCV ’02, CVPR ’03) and Hayman (ECCV ’04) this framework is found to be more accurate and efficient.