一种改进卷积神经网络的教学图像检索方法

doi:10.19665/j.issn1001-2400.2019.03.009

Abstract

Abstract:

Aiming at the loss of feature information caused by the convolution neural network in extracting image features and the reduction of high-dimensional image feature data, an image retrieval optimization scheme based on the improved convolution neural network is proposed. First, the image features are extracted through the convolutional layer of fusion, and a full connection layer is added between the convolutional layers of fusion to reduce the loss of image feature information. Then the PCA is used to effectively reduce the dimension of high-dimensional characteristic data and the high-dimensional feature vectors are mapped to the low-dimensional vector space. Finally, the cosine similarity method is used to match the similarity to achieve similar image retrieval. The proposed method is compared with classical methods such as LeNet-L and LeNet-5 in the performance of image retrieval. Experimental results show that the proposed retrieval method improves the recall rate and the average precision rate by at least 3%27.3% compared with classical methods.

Key words: image retrieval, convolution neural network, feature extraction, principal component analysis

CLC Number:

TP399

LIU Daohua,CUI Yushuang,ZHAO Yansong,SONG Yuting,WANG Jinghui. Method for retrieving the teaching image based on the improved convolutional neural network[J].Journal of Xidian University, 2019, 46(3): 52-58.

Figures/Tables 5

References 11

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比较项	Top10	Top20	Top30	Top40	Top50
LeNet-5	0.781 3	0.721 6	0.686 3	0.650 3	0.638 5
Method	0.900 6	0.783 5	0.686 3	0.643 2	0.596 4
CNN	0.906 8	0.859 5	0.789 7	0.713 0	0.669 2
LeNet-L	0.908 9	0.862 5	0.839 5	0.819 3	0.799 7
文中方法	0.950 0	0.943 7	0.912 2	0.907 3	0.869 5

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Method for retrieving the teaching image based on the improved convolutional neural network

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