In the construction of 2 million furniture image databases, aiming at the problem of low quality of database, a combination of CNN and Metric learning algorithm is proposed, which makes it possible to quickly and accurately remove duplicate and irrelevant samples in the furniture image database. Solve problems that images screening method is complex, the accuracy is not high, time-consuming is long. Deep learning algorithm achieve excellent image matching ability in actual furniture retrieval applications after improving data quality.
REFERENCES
1.
Habib
I
, Tarraf
A
, Saadawi
T.A
neural network controller for congestion control in ATM multiplexers
[J]. Computer Networks & Isdn Systems
, 1997
, 29
(3
):325
–334
.2.
Kelly
F P
, Maulloo
A K
, Tan
D K H.
Rate control for communication networks: shadow prices, proportional fairness and stability
[J]. Journal of the Operational Research Society
, 1998
, 49
(3
):237
–252
.3.
Han
Fengqing
1, Song
Zhijian
, YU
Rui
. Fast Deduplication for Massive Images
[J]. Journal of Computer Applications.
2016
. 07
:1797
–1800
(in chinese)4.
Razavian
A S
, Azizpour
R H
, Sullivan
J
, et al. CNN features off-the-shelf: an astounding baseline for recognition
. In IEEE Conference on Computer Vision and Pattern Recognition Workshops
, 2014
[C] [S.l.]: 512
–519
.5.
Wan
J
, Wang
D
, Hoi
SCH
, et al. Deep learning for content-based image retrieval: a comprehensive study
. In ACM International Conference on Multimedia In Acm International Conference on Multimedia
2014
[C]: 157
–166
.6.
Song
H O
, Xiang
Y
, Jegelka
S
, Savarese
S.
Deep metric learning via lifted structured feature embedding
. In IEEE Conference on Computer Vision and Pattern Recognition
, 2016
[C] [S.l.]:4004
–4012
.7.
Babenko
A
, Slesarev
A
, Chigorin
A
, et al. Neural Codes for Image Retrieval
[C]// European Conference on Computer Vision
. Springer
, Cham
, 2014
:584
–599
.8.
Krizhevsky
A
, Sutskever
I
, Hinton
G E.
ImageNet classification with deep convolutional neural networks
[C]// International Conference on Neural Information Processing Systems
. Curran Associates Inc
. 2012
:1097
–1105
.9.
Schroff
F
, Kalenichenko
D
, Philbin
J.
Facenet: a unified embedding for face recognition and clustering
. In IEEE Conference on Computer Vision and Pattern Recognition
, 2015
[C] [S.l.].
This content is only available via PDF.
© 2018 Author(s).
2018
Author(s)
