A Multi-view Image Sets Classification Based on Graph Convolutional Neural Network. Issue 1 (June 2021) - Liu, Xun; Chen, XiaoLin; Xia, GuoQing; Chen, HuaZhen; Ye, HeZhong; Chen, ZhanChi
Abstract: Multi-view images represent the target object from various perspectives. We propose a new spectral graph convolutional neural network (BSGCN) for multi-view image sets, which uses spectral graph convolution technology to process multi-view image sets. With the batch normalization techniques, the BSGCN speeds up the model convergence and improves the generalization capabilities of the model. We analyze the performance of two variants of BSGCN and its model by experiments on multi-view datasets. Experimental results show that the BSGCN is 2.84% and 7.26% higher than the classic graph CNN [17] on Modelnet10 and Modelnet40 multi-view datasets, respectively, and 0.34% higher than the classical CNN [6] on Modelnet40 multi-view dataset.