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X. Hao et al.
Afterward, we employ the attention map to remove valuable parts from the
original pedestrian sample and therefore obtain a large number of samples with
valuable part absence. Finally, we combine original pedestrian samples and new
pedestrian samples to fine-tune the base network model so that the network
model can learn new discriminative features from valuable absence parts of the
sample. Experimental results have shown that the proposed method achieves
better performance than other state-of-the-art methods.
Acknowledgements. This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, Natural Science
Foundation of Tianjin under Grant No. 17JCZDJC30600, the Fund of Tianjin Normal
University under Grant No.135202RC1703, the Open Projects Program of National
Laboratory of Pattern Recognition under Grant No. 201800002, and the Tianjin Higher
Education Creative Team Funds Program.
References
1. Liao S, Hu Y, Zhu X, Li S (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE conference on computer vision
and pattern recognition, pp 2197–2206
2. Liu S, Hao X, Zhang Z (2018) Pedestrian retrieval via part-based gradation regularization in sensor networks. IEEE Access 6:38171–38178
3. Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C (2013) Cross-view action recognition via a continuous virtual path. In: IEEE conference on computer vision and
pattern recognition, pp 2690–2697
4. Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2014) Cross-view action recognition
using contextual maximum margin clustering. IEEE Trans Circ Syst Video Technol
24(10):1663–1668
5. Li A, Wu Z, Lu H, Chen D, Sun G (2018) Collaborative self-regression method
with nonlinear feature based on multi-task learning for image classification. IEEE
Access 6:43513–43525
6. Li A, Chen D, Wu Z, Sun G, Lin K (2018) Self-supervised sparse coding scheme
for image classification based on low rank representation. PLOS One 13(6):1–15
7. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations
with domain guided dropout for person re-identification. In: IEEE conference on
computer vision and pattern recognition, pp 1249–1258
8. Zheng Z, Zheng L, Yang Y (2017) A discriminatively learned CNN embedding for
person re-identification. ACM Trans Multimedia Comput Commun Appl 14(1):1–
20
9. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person
retrieval with refined part pooling (and a strong convolutional baseline). In: European conference on computer vision, pp 1–17
10. Zhong Z, Zheng L, Li S, Yang Y (2018) Generalizing a person retrieval model
hetero-and homogeneously. In: European conference on computer vision, pp 172–
188
11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep
convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105
X. Hao et al.
Afterward, we employ the attention map to remove valuable parts from the
original pedestrian sample and therefore obtain a large number of samples with
valuable part absence. Finally, we combine original pedestrian samples and new
pedestrian samples to fine-tune the base network model so that the network
model can learn new discriminative features from valuable absence parts of the
sample. Experimental results have shown that the proposed method achieves
better performance than other state-of-the-art methods.
Acknowledgements. This work was supported by National Natural Science Foundation of China under Grant No. 61501327 and No. 61711530240, Natural Science
Foundation of Tianjin under Grant No. 17JCZDJC30600, the Fund of Tianjin Normal
University under Grant No.135202RC1703, the Open Projects Program of National
Laboratory of Pattern Recognition under Grant No. 201800002, and the Tianjin Higher
Education Creative Team Funds Program.
References
1. Liao S, Hu Y, Zhu X, Li S (2015) Person re-identification by local maximal occurrence representation and metric learning. In: IEEE conference on computer vision
and pattern recognition, pp 2197–2206
2. Liu S, Hao X, Zhang Z (2018) Pedestrian retrieval via part-based gradation regularization in sensor networks. IEEE Access 6:38171–38178
3. Zhang Z, Wang C, Xiao B, Zhou W, Liu S, Shi C (2013) Cross-view action recognition via a continuous virtual path. In: IEEE conference on computer vision and
pattern recognition, pp 2690–2697
4. Zhang Z, Wang C, Xiao B, Zhou W, Liu S (2014) Cross-view action recognition
using contextual maximum margin clustering. IEEE Trans Circ Syst Video Technol
24(10):1663–1668
5. Li A, Wu Z, Lu H, Chen D, Sun G (2018) Collaborative self-regression method
with nonlinear feature based on multi-task learning for image classification. IEEE
Access 6:43513–43525
6. Li A, Chen D, Wu Z, Sun G, Lin K (2018) Self-supervised sparse coding scheme
for image classification based on low rank representation. PLOS One 13(6):1–15
7. Xiao T, Li H, Ouyang W, Wang X (2016) Learning deep feature representations
with domain guided dropout for person re-identification. In: IEEE conference on
computer vision and pattern recognition, pp 1249–1258
8. Zheng Z, Zheng L, Yang Y (2017) A discriminatively learned CNN embedding for
person re-identification. ACM Trans Multimedia Comput Commun Appl 14(1):1–
20
9. Sun Y, Zheng L, Yang Y, Tian Q, Wang S (2018) Beyond part models: person
retrieval with refined part pooling (and a strong convolutional baseline). In: European conference on computer vision, pp 1–17
10. Zhong Z, Zheng L, Li S, Yang Y (2018) Generalizing a person retrieval model
hetero-and homogeneously. In: European conference on computer vision, pp 172–
188
11. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep
convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105