Abstract：In this paper,a multi-view convolutional neural network model(MV-PearlNet)is proposed,which can replace artificial fine-grained pearl classification.The model uses parallel processing to extract features from multiple perspective pictures of pearls,which can improve the effect of feature extraction.In addition,MV-PearlNet model adopts the feature fusion of the middle layer as the feature expression of the pearl.When the amount of data in the training set is limited,MV-PearlNet is combined with K-means method in this paper.And,we apply the unsupervised clustering algorithm to the extracted features,when the similarity calculation is used to complete the automatic class-based learning.It is worth pointing out that the proposed MV-PearlNet model expands the data set and improves the under-fitting problem of the deep classification model due to insufficient training set.Therefore,it can improve the classification accuracy of the model.Experimental data indicates that MV-PearlNet has significantly improved the classification accuracy of pearl fine-grained pictures,compared with mainstream convolutional neural network models.
钱涛,熊晖,陈晋音. 基于MV-PearlNet的珍珠细粒度分类方法[J]. 小型微型计算机系统, 2021, 42(1): 185-190.
QIAN Tao,XIONG Hui,CHEN Jin-yin. Pearl Fine-grain Classification Method Based on MV-PearlNet. Journal of Chinese Computer Systems, 2021, 42(1): 185-190.
2021, Vol. 42 
