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

作　　者：Xiaoxia Yang Yisheng Gao Shuhua Zhang Zhedong Ge Yucheng Zhou 

机构地区：[1]School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan,250101,China [2]School of Architecture and Urban Planning,Shandong Jianzhu University,Jinan,250101,China

出　　处：《Journal of Renewable Materials》2022年第12期3587-3598,共12页可再生材料杂志（英文）

基　　金：The Natural Science Foundation of Shandong Province,China(Grant No.ZR2020QC174);The Application of Computed Tomography(CT)Scanning Technology to Damage Detection of Timber Frames of Architectural Heritage.The Taishan Scholar Project of Shandong Province,China(Grant No.2015162).

摘　　要：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 CTOSS 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 fea-tures 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_(P,R)^(u2)+GLCM+Tamura,LBP_(P,R)^(ri)+GLCM+Tamura and LBP_(P,R)^(riu2)+GLCM+Tamura were obtained.The five fused features were classified by extreme learning machine and BP neural network.The clas-sification effect of feature LBP_(P,R)^(u2)+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 classfcation eficiency is high.In condusion,the LBP_(P,R)^(u2)+GLCM+Tamura method com-bined with extreme learning machine can be used as a quick and acurate classifier,providing an efficient and feasible class ification method of rosewood.

关 键 词：ROSEWOOD micro CT feature fusion ELM BP neural network 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]