关键词: 共生点对, 局部三值模式, 成对旋转不变, 噪声鲁棒性, 自适应阈值

Abstract:

The texture feature extraction algorithm of Pairwise Rotation Invariant Co-occurrence Local Binary Pattern（PRICoLBP） has characteristics of poor rotation invariance, and its improved algorithm EPRICoELBP can enhance rotation invariance effectively, but it is sensitive to illumination change and noise. In order to solve the issues, an enhanced pairwise rotation-invariant co-occurrence adaptive complete local ternary pattern is proposed. Firstly, the image is divided into Upper pattern and Lower pattern by Adaptive Local Ternary Pattern（ALTP）. Secondly, the neighborhood initial coding points corresponding to the maximum and minimum LBP features of the pixels are found in the Upper pattern and Lower pattern respectively. Thirdly, the context co-occurrence point pair of the central pixel point is determined by using the central pixel point and the neighborhood initial points of coding corresponding to the maximum and minimum LBP values of each pixel, respectively. Fourth, the local texture information of co-occurrence point pairs in Upper pattern and Lower pattern is extracted using Adaptive Complete Local Ternary Pattern（ACLTP）. At last, a Chi-Square Kernel Support Vector Machine, which is trained with feature histogram of co-occurrence point pairs in context to detect the image texture categories. Compared with the original PRICoLBP algorithm and other algorithms, the proposed algorithm has a obvious improvement in classification accuracy on the Brodatz, Outex（TC10, TC12-h, TC12-t, TC14）, CUReT, KTH_TIPS, UIUC texture databases, and on the KTH_TIPS texture databases with Gaussian noise and salt-and-pepper noise, the proposed algorithm still maintains a high classification accuracy. The experimental results show that the proposed algorithm is highly robust to rotation, illumination changes and noise.

Key words: co-occurrence point pairs, local ternary pattern, pairwise rotation-invariant, noise robustness, adaptive threshold