Person Re-identification by Local Maximal Occurrence Representation and Metric Learning

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An effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA) are proposed for person re-identification. The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes.

The LOMO feature extraction process

Besides, to handle illumination variations, we apply the Retinex transform and the Scale Invariant Local Ternary Pattern (SILTP). To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2\%, 4.88\%, 28.91\%, and 31.55\% on the four databases, respectively.



CMC curves on the VIPeR database CMC curves on the CUHK01 database

CMC curves on the VIPeR database

CMC curves on the CUHK01 (CUHK Campus) database

Table 1. Summary of results (%) for the proposed LOMO+XQDA algorithm

Database Rank 1 Rank 5 Rank 10 Rank 15 Rank 20
VIPeR 40.00 68.13 80.51 87.37 91.08
QMUL Grid 16.56 33.84 41.84 47.68 52.40
CUHK01 63.21 83.89 90.04 92.59 94.16
CUHK03 Labeled 52.20 82.23 92.14 94.74 96.25
CUHK03 Detected 46.25 78.90 88.55 92.30 94.25

Note: the source code package contains the CMC curves for performance plot.


Shengcai Liao,

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences.


[1]Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z. Li, "Person Re-identification by Local Maximal Occurrence Representation and Metric Learning." In IEEE International Conference on Computer Vision and Pattern Recognition, June 7-12, Boston, Massachusetts, USA, 2015. [pdf] [poster]

Last updated: May 7, 2015