Texture image classification based on using descriptor of indexes

Abstract

A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and convolved with the polynomial approximation kernels of various degrees p. As a result, a set of approximations is obtained. For each element of the source vector, these approximations are ranked according to the difference between the original and approximated values. A set of indices of polynomial degrees form a local feature. This procedure is repeated for each pixel from the local area. Finally, a proposed texture descriptor is obtained from the frequency histogram of all obtained local features. A nearest neighbor classifier utilizing correlation distance metric is used to evaluate a performance of the introduced descriptor. An accuracy of texture classification is evaluated on the Brodatz dataset, with respect to different methods of texture analysis and classification. The results of this comparison show that the proposed method is competitive with the recent statistical approaches such as local binary patterns (LBP), local ternary patterns, completed LBP, Weber's local descriptor, and VZ algorithms (VZ-MR8 and VZ-Joint).