Person Re-identification by Mid-level Attribute and Part-based Identity Learning

Guopeng Zhang, Jinhua Xu
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:220-231, 2018.

Abstract

Existing deep models using attributes usually take global features for identity classification and attribute recognition. However, some attributes exist in local position, such as a hat and shoes, therefore global feature alone is insufficient for person representation. In this work, we propose to use the attribute recognition as an auxiliary task for person re-identification. The attributes are recognised from the local regions of mid-level layers. Besides, we extract local features and global features from a high-level layer for identity classification. The mid-level attribute learning improves the discrimination of high-level features, and the local feature is complementary to the global feature. We report competitive results on two large-scale person re-identification benchmarks, Market-1501 and DukeMTMC-reID datasets, which demonstrate the effectiveness of the proposed method.

Cite this Paper

BibTeX
@InProceedings{pmlr-v95-zhang18c, title = {Person Re-identification by Mid-level Attribute and Part-based Identity Learning}, author = {Zhang, Guopeng and Xu, Jinhua}, booktitle = {Proceedings of The 10th Asian Conference on Machine Learning}, pages = {220--231}, year = {2018}, editor = {Zhu, Jun and Takeuchi, Ichiro}, volume = {95}, series = {Proceedings of Machine Learning Research}, month = {14--16 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v95/zhang18c/zhang18c.pdf}, url = {https://proceedings.mlr.press/v95/zhang18c.html}, abstract = {Existing deep models using attributes usually take global features for identity classification and attribute recognition. However, some attributes exist in local position, such as a hat and shoes, therefore global feature alone is insufficient for person representation. In this work, we propose to use the attribute recognition as an auxiliary task for person re-identification. The attributes are recognised from the local regions of mid-level layers. Besides, we extract local features and global features from a high-level layer for identity classification. The mid-level attribute learning improves the discrimination of high-level features, and the local feature is complementary to the global feature. We report competitive results on two large-scale person re-identification benchmarks, Market-1501 and DukeMTMC-reID datasets, which demonstrate the effectiveness of the proposed method.} }
Endnote
%0 Conference Paper %T Person Re-identification by Mid-level Attribute and Part-based Identity Learning %A Guopeng Zhang %A Jinhua Xu %B Proceedings of The 10th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2018 %E Jun Zhu %E Ichiro Takeuchi %F pmlr-v95-zhang18c %I PMLR %P 220--231 %U https://proceedings.mlr.press/v95/zhang18c.html %V 95 %X Existing deep models using attributes usually take global features for identity classification and attribute recognition. However, some attributes exist in local position, such as a hat and shoes, therefore global feature alone is insufficient for person representation. In this work, we propose to use the attribute recognition as an auxiliary task for person re-identification. The attributes are recognised from the local regions of mid-level layers. Besides, we extract local features and global features from a high-level layer for identity classification. The mid-level attribute learning improves the discrimination of high-level features, and the local feature is complementary to the global feature. We report competitive results on two large-scale person re-identification benchmarks, Market-1501 and DukeMTMC-reID datasets, which demonstrate the effectiveness of the proposed method.
APA
Zhang, G. & Xu, J.. (2018). Person Re-identification by Mid-level Attribute and Part-based Identity Learning. Proceedings of The 10th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 95:220-231 Available from https://proceedings.mlr.press/v95/zhang18c.html.

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