An efficient method for rotation and scaling invariant texture classification

Abstract

This paper presents a new approach for texture classification using rotation and scaling invariant parameters. A test textured image can be correctly classified even if it is rotated and scaled. Based on a 2-D Wold-like decomposition of homogeneous random fields, the texture field can be decomposed into a deterministic component and an indeterministic component. The spectral density function (SDF) of the former is a sum of 1-D or 2-D delta functions. The 2-D autocorrelation function (ACF) of the latter is fitted to the assumed anisotropic ACF that has an elliptical contour. Invariant parameters applicable to the classification of rotated and scaled textured images can be estimated by combining the parameters representing the ellipse and those representing the delta functions. The effectiveness of this method is illustrated through experimental results on natural textures.