A texture descriptor based on a set of indices of degrees of local approximating polynomials is proposed in this paper.
First, a method to construct 2D local polynomial approximation kernels (k-LPAp) for arbitrary polynomials of degree p
is presented. An image is split into non-overlapping patches, reshaped into one-dimensional source vectors and
convolved with the polynomial approximation kernels of various degrees. 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. Finally, a proposed texture descriptor is obtained from the frequency histogram of all obtained local
features. A nearest neighbor classifier utilizing Chi-square distance metric is used to evaluate a performance of the
introduced descriptor. An accuracy of texture classification is evaluated on the following datasets: Brodatz, KTH-TIPS,
KTH-TIPS2b and Columbia-Utrecht (CUReT) 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 (VZMR8
and VZ-Joint). At the same time, on KTH-TIPS2-b and KTH-TIPS datasets, the proposed method is slightly
inferior to some of the state-of-the-art methods.