Research on Rosewood Micro Image Classification Method Based on Feature Fusion and ELM

Rosewood is a kind of high-quality and precious wood in China. The correct identification of rosewood species is of great significance to the import and export trade and species identification of furniture materials. In this paper, micro CT was used to obtain the micro images of cross sections, radial sections and tangential sections of 24 kinds of rosewood, and the data sets were constructed. PCA method was used to reduce the dimension of four features including logical binary pattern, local configuration pattern, rotation invariant LBP, uniform LBP. These four features and one feature not reducing dimension (rotation invariant uniform LBP) was fused with Gray Level Co-Occurrence Matrix and Tamura features, respectively, a total of five fused features LBP+GLCM+Tamura, LCP +GLCM+Tamura, LBP,Ru2 +GLCM+Tamura, LBPP,Rri+GLCM+Tamura and LBPP,Rriu2 +GLCM+Tamura were obtained. The five fused features were classified by extreme learning machine and BP neural network. The classification effect of feature LBPP,Ru2 +GLCM+Tamura combined with extreme learning machine was the best, and the classification accuracy of cross, radial and tangential sections reached 100%, 97.63% and 94.72%, respectively, which is 0.83%, 2.77% and 5.70% higher than that of BP neural network. The classification running time of ELM is less than 1 s, and the classification efficiency is high. In conclusion, the LBPP,Ru2+GLCM+Tamura method combined with extreme learning machine can be used as a quick and accurate classifier, providing an efficient and feasible classification method of rosewood.