A Klein-Bottle-Based Dictionary for Texture Representation

摘要

A natural object of study in texture representation and material classification is the probability density function, in pixel-value space, underlying the set of small patches from the given image. Inspired by the fact that small \(n\times n\) high-contrast patches from natural images in gray-scale accumulate with high density around a surface \(\fancyscript{K}\subset {\mathbb {R}}^{n^2}\) with the topology of a Klein bottle (Carlsson et al. International Journal of Computer Vision 76(1):1–12, 2008), we present in this paper a novel framework for the estimation and representation of distributions around \(\fancyscript{K}\), of patches from texture images. More specifically, we show that most \(n\times n\) patches from a given image can be projected onto \(\fancyscript{K}\) yielding a finite sample \(S\subset \fancyscript{K}\), whose underlying probability density function can be represented in terms of Fourier-like coefficients, which in turn, can be estimated from \(S\). We show that image rotation acts as a linear transformation at the level of the estimated coefficients, and use this to define a multi-scale rotation-invariant descriptor. We test it by classifying the materials in three popular data sets: The CUReT, UIUCTex and KTH-TIPS texture databases.