Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 61-66.doi: 10.6040/j.issn.1672-3961.0.2017.432

• Machine Learning & Data Mining • Previous Articles     Next Articles

Images auto-encoding algorithm based on deep convolution neural network

Yijiang HE1(),Junping DU1,*(),Feifei KOU1,Meiyu LIANG1,Wei WANG2,Ang LUO2   

  1. 1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
    2. Sina. Com Technology (China) Corporation, Beijing 100876, China
  • Received:2017-05-05 Online:2019-04-20 Published:2019-04-19
  • Contact: Junping DU E-mail:he66024748@163.com;junpingdu@126.com
  • Supported by:
    国家自然科学基金重点项目(61532006);国家自然科学基金国际合作项目(61320106006);国家自然科学基金青年科学基金(61502042)

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Abstract:

At present, image coding research was focused on information lossless, but it did not reflect the social network image differentiation. A novel social network images auto-encoding algorithm based on deep convolution neural network was proposed. The algorithm obtained good performance on image auto-encoding, which combined the feature extraction ability of deep convolutional neural network and characteristics of images in social networks. It combined the characteristics of the social network image with the clustering algorithm to cluster social network image and got the distance information, next the deep convolutional neural network was used to learn the distance information of these images, then it extracted the fully connected layer in the deep convolution neural network as the image coding, repeated the above steps and got the image coding finally. The experimental results showed that the proposed algorithm performed better than other algorithms of image search, and was more adaptive in the social network image search than that of the other algorithms mentioned.

Key words: deep convolution, neural network, social network pictures, image auto-encoding, image search

CLC Number: 

  • TP37

Fig.1

The general framework of auto-coding algorithm for social network image based on deep convolution neural network"

Fig.2

Distance information extraction"

Fig.3

Deep convolution neural network coding learning and generation process"

Table 1

Deep convolution neural network architecture"

层数 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
功能 Input Conv3-64 Conv3-64 Maxpool Conv3-128 Conv3-128 Maxpool Conv3-256 Conv3-256 Conv3-256 Maxpool Conv3-512 Conv3-512 Conv3-512 Maxpool Conv3-512 Conv3-512 Conv3-512 FC-4 096 FC-2 048 FC-512 Softmax

Table 2

Comparison of the precision @5of different algorithms on social network images"

算法 16位 32位 64位
DA 0.538 0.532 0.584
AEVB 0.554 0.422 0.356
DCNNSE-1 0.330 0.566 0.590
DCNNSE-2 0.572 0.606 0.596

Fig.4

The Precision@5 histogram of different algorithms on the social network image"

Table 3

Comparison of MAP@5 of different algorithms on social network images"

算法 16位 32位 64位
DA 0.180 0.202 0.218
AEVB 0.200 0.176 0.162
DCNNSE-1 0.138 0.204 0.209
DCNNSE-2 0.212 0.244 0.212

Fig.5

The MAP@5 histogram of different algorithms on the social network image"

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