多通道多模式融合LBP特征的纹理相似度计算

exture similarity calculation is one of the basic methods of big-data analysis and data mining. For solving the problem that the existing texture features are not strong for color image discrimination, a texture similarity calculation method with improved local binary pattern features is proposed. This method proposes three modes for feature fusion, including extreme mode, addition mode and encoding mode. The LBP features acquired on the three channels of H, S and V of color image are fused by these modes to obtain the texture description of color image. The fusion operation is carried out in three stages including LBP calculation of neighborhood pixels, LBP calculation of central pixels, and histogram feature extraction, to improve the ability of feature discrimination. The results of texture similarity experiments on VisTex texture database show that, the false acceptance rate, flase rejection rate and equal error rate of this method are obviously lower than those of methods described in references [7, 8, 9].