TY - GEN
T1 - Person re-identification using data-driven metric adaptation
AU - Wang, Zheng
AU - Hu, Ruimin
AU - Liang, Chao
AU - Jiang, Junjun
AU - Sun, Kaimin
AU - Leng, Qingming
AU - Huang, Bingyue
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. In person re-identification procedure, choosing a proper distance metric is a crucial aspect [2]. Traditional methods always utilize a uniform learned metric, which ignored specific constraints given by this re-identification task that the learned metric is highly prone to over-fitting [21], and each person holding their unique characteristic brings inconsistency. Therefore, it is obviously inappropriate to merely employ a uniform metric. In this paper, we propose a data-driven metric adaptation method to improve the uniform metric. The key novelty of the approach is that we re-exploits the training data with cross-view consistency to adaptively adjust the metric. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with a significant improvement over baseline methods.
AB - Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. In person re-identification procedure, choosing a proper distance metric is a crucial aspect [2]. Traditional methods always utilize a uniform learned metric, which ignored specific constraints given by this re-identification task that the learned metric is highly prone to over-fitting [21], and each person holding their unique characteristic brings inconsistency. Therefore, it is obviously inappropriate to merely employ a uniform metric. In this paper, we propose a data-driven metric adaptation method to improve the uniform metric. The key novelty of the approach is that we re-exploits the training data with cross-view consistency to adaptively adjust the metric. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with a significant improvement over baseline methods.
KW - Cross-view consistency
KW - Data-driven metric adaptation
KW - Person re-identification
UR - https://www.scopus.com/pages/publications/84927799920
U2 - 10.1007/978-3-319-14442-9_17
DO - 10.1007/978-3-319-14442-9_17
M3 - 会议稿件
AN - SCOPUS:84927799920
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 195
EP - 207
BT - MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
A2 - He, Xiangjian
A2 - Tao, Dacheng
A2 - Hasan, Muhammad Abul
A2 - Luo, Suhuai
A2 - Xu, Changsheng
A2 - Yang, Jie
PB - Springer Verlag
T2 - 21st International Conference on MultiMedia Modeling, MMM 2015
Y2 - 5 January 2015 through 7 January 2015
ER -