International Core Journal of Engineering 2020-26 | Page 87
澳
method is how to optimize the synthesis algorithm to
minimize the distribution difference between the synthetic
image and the real image. Besides, based on the training
strategy of using synthetic and real mixed data first, and then
using real images to perform fine tuning (fusion + RealImg),
the model will achieve better training results (mAP 58.9%).
Fig. 8 shows partial results of the proposed synthesis
algorithm on the FlickrLogos-32 test set. It can be found that
the algorithm is robust to multi-scale, multi-view, rotational
deformation and partial occlusion of some object.
Fig. 8. Qualitative evaluation on FlickrLogo-32.
[9]
IV. C ONCLUSION
In this work, aim at the problem of lack of annotation
data in the logo recognition task under the deep learning
framework. From the perspective of automatic synthesis of
large-scale training data, a context-based logo image
synthesis algorithm is proposed based on the existing
synthetic ideas. Through detailed experiments on the
FlickrLogos-32, it is shown that when only a small amount
of annotation data is available, the proposed synthesis
algorithm can greatly improve (mAP 8.5%) the performance
of the logo recognition algorithm without relying on
additional manual annotation, fully validated the
effectiveness and superiority of the proposed algorithm.
[10]
[11]
[12]
[13]
[14]
[15]
[16]
R EFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[17]
Y. Fu, Beijing Jiaotong University, 2016.
Y. Gao, F. Wang, H. Luan, T.S. Chua, International Conference on
Multimedia Retrieval. ACM, vol. 169, 2014.
C. Pan, Z. Yan, X. Xu, M. Sun, J. Shao, D. Wu, IET International
Conference on Smart and Sustainable City, 123, 2013.
K. He, G. Gkioxari, P. Dollar, R. Girshick, International Conference
on Computer Vision. IEEE, vol. 2980, 2017.
X. Wang, A. Shrivastava, A. Gupta, Conference on Computer Vision
and Pattern Recognition. IEEE, 3039, 2017.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu, A.C.
Berg, Springer International Publishing, 21, 2016.
A. Joly, O. Buisson, International Conference on Multimedia, 581,
2009.
Y. Kalantidis, L.G. Pueyo, M. Trevisiol, R.V. Zwol, Y. Avrithis,
ACM International Conference on Multimedia Retrieval, vol. 1, 2011.
[18]
[19]
[20]
[21]
[22]
[23]
65
S. Romberg, L.G. Pueyo, R. Lienhart, R.V. Zwol, ACM International
Conference on Multimedia Retrieval, vol. 1, 2011.
S.C.H. Hoi, X. Wu, H. Liu, Y. Wu, H. Wang, H. Xue, Q. Wang,
IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 5,
no. 2403, 2015.
S. Bianco, M. Buzzelli, D. Mazzini, R. Schettini, Neurocomputing, C,
vol. 23, 2017.
H. Su, X. Zhu, S. Gong, IEEE Winter Conference on Applications of
Computer Vision, no. 530, 2017.
A. Gupta, A. Vedaldi, A. Zisserman, IEEE Computer Vision and
Pattern Recognition, no. 2315, 2016.
M. Jaderberg, K. Simonyan, A. Vedaldi, A. Zisserman, International
Journal of Computer Vision, vol. 1, no. 1, 2016.
G. Georgakis, A. Mousavian, A.C. Berg, J. Kosecka, arXiv preprint
arXiv, vol. 1702, no. 07836, 2017.
C. Eggert, A. Winschel, R. Lienhart, ACM International Conference
on Multimedia, vol. 1283, 2015.
Y. Bengio, R. Collobert, J. Weston, ACM International Conference
on Machine Learning, no. 41, 2009.
A. Oliva, A. Torralba, Trends in Cognitive Sciences, vol. 12, no. 520,
2007.
R. Mottaghi, X. Chen, X. Liu, N.G. Cho, S.W. Lee, S. Fidler, R.
Urtasun, A. Yuille, IEEE Computer Vision and Pattern Recognition,
no. 891, 2014.
H. Katti, M.V. Peelen, S.P. Arun, Attention Perception &
Psychophysics, vol. 2, no. 1, 2017.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, A. Torralba, IEEE
Transactions on Pattern Analysis and Machine Intelligence, vol. 6, no.
1452, 2018.
J. Gu, S. Gould, arXiv preprint arXiv, vol. 1506, no. 07224, 2015.
G. Oliveira, X. Frazão, A. Pimentel, B. Ribeiro, IEEE International
Joint Conference on Neural Networks, vol. 985, 2016.