Local feature descriptor based on 2D local polynomial approximation kernel indices

A. I. Sherstobitov; V. I. Marchuk; D. V. Timofeev; V. V. Voronin; K. O. Egiazarian

doi:10.1117/12.2041610

25 February 2014 Local feature descriptor based on 2D local polynomial approximation kernel indices

A. I. Sherstobitov, V. I. Marchuk, D. V. Timofeev, V. V. Voronin, K. O. Egiazarian

Proceedings Volume 9019, Image Processing: Algorithms and Systems XII; 901908 (2014) https://doi.org/10.1117/12.2041610
Event: IS&T/SPIE Electronic Imaging, 2014, San Francisco, California, United States

Abstract

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.

Citation Download Citation

A. I. Sherstobitov, V. I. Marchuk, D. V. Timofeev, V. V. Voronin, and K. O. Egiazarian "Local feature descriptor based on 2D local polynomial approximation kernel indices", Proc. SPIE 9019, Image Processing: Algorithms and Systems XII, 901908 (25 February 2014); https://doi.org/10.1117/12.2041610

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