关键词: 微分特征, 小波分解, 抗模糊, 旋转不变性, 鲁棒纹理表达

Abstract: Aiming at the problem that traditional local binary pattern operators lack deep-level correlation information between pixels and have poor robustness to common blurring and rotation changes in images, a robust texture expression operator combining differential features and Haar wavelet decomposition was proposed. In the differential feature channel, the first-order and second-order differential features in the image were extracted by the isotropic differential operators, so that the differential features of the image were essentially invariant to rotation and robust to image blur. In the wavelet decomposition feature extraction channel, based on the characteristic that the wavelet transform has good localization in the time domain and frequency domain at the same time, multi-scale two-dimensional Haar wavelet decomposition was used to extract blurring robustness features. Finally, the feature histograms on the two channels were concatenated to construct a texture description of the image. In the feature discrimination experiments, the accuracy of the proposed operator on the complex UMD, UIUC and KTH-TIPS texture databases reaches 98.86%, 98.2% and 99.05%, respectively, and compared with that of the MRELBP (Median Robust Extended Local Binary Pattern) operator, the accuracy increases by 0.26%, 1.32% and 1.12% respectively. In the robustness analysis experiments on rotation change and image blurring, the classification accuracy of the proposed operator on the TC10 texture database with only rotation changes reaches 99.87%, and the classification accuracy decrease of the proposed operator on the TC11 texture database with different levels of Gaussian blurs is only 6%. In the computational complexity experiments, the feature dimension of the proposed operator is only 324, and the average feature extraction time of the proposed operator on the TC10 texture database is 30.9 ms. Experimental results show that the method combining differential feature and Haar wavelet decomposition has strong feature discriminability and strong robustness to rotation and blurring, as well as has low computational complexity. It has good applicability in situations with small database.

Key words: differential feature, wavelet decomposition, anti-blurring, rotation invariance, robust texture representation