Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (6): 2045-2050.doi: 10.13229/j.cnki.jdxbgxb20181057

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Similarity retention instance retrieval method

Hong-wei ZHAO1,2(),Peng WANG1,Li-li FAN1,Huang-shui HU3,Ping-ping LIU1()   

  1. 1. College of Computer Science and Technology，Jilin University，Changchun 130012，China
    2. State Key Laboratory of Applied Optics，Chinese Academy of Science，Changchun 130033，China
    3. School of Computer Science and Engineering，Changchun University of Technology，Changchun 130012，China
  • Received:2018-10-22 Online:2019-11-01 Published:2019-11-08
  • Contact: Ping-ping LIU E-mail:zhaohw@jlu.edu.cn;liupp@jlu.edu.cn

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Abstract:

Aiming at the network training of multi-input samples, a similarity retention instance retrieval method is proposed. Firstly, the input image features are extracted by the convolution structure in the depth network and pooled. Then, according to the benchmark order, the similarity relationship between the low correlation image and the query image is corrected, and the low correlation image contrast loss coefficient is obtained, and the loss value within the loss reference value is retained. The loss value is performed to maintain contrast loss training based on similarity. Finally, the post-training network is used to extract image features for instance-level image retrieval. The experimental results show that the loss comparison function based on similarity is feasible, and the method significantly improves the accuracy of instance-level image retrieval.

Key words: computer application, image retrieval, convolutional neural network(CNN), loss function, sorting learning, similarity retention

CLC Number: 

  • TP391

Fig.1

Overall flow chart for depth image retrievalwith similarity retention"

Fig.2

Schematic diagram of similarity retentioncontrast loss network"

Fig.3

Loss curves"

Fig.4

Select low correlation image"

Table 1

Pooling method selection"

池化方法 初始k 数据集
Oxford5k Paris6k
MAC 正无穷 54.93 66.06
SPoC 1 45.63 66.46
GeM 3 57.95 69.29

Fig.5

Curves comparison on the dataset"

Table 2

Post training phase effect"

网络主干 特征维度 后处理方法

Oxford5k

数据集

Paris6k

数据集
MAC GeM MAC Gem
AlexNet 256 - 54.93 57.95 66.18 68.19
PCA 58.21 66.15 67.61 71.78

Table 3

Comparison of experimental results"

方法 特征维度 数据集
Oxford5k Paris6k
文献[19] 1024 56.0 -
文献[19] 128 43.3 -
文献[20] 128 55.7 -
文献[21] 256 53.3 67.0
文献[13] 256 53.1 -
文献[22] 128 59.3 59.0
文献[23] 256 56.5 -
本文 256 66.15 71.78
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