Description: Person re-identification is an important technique towards
automatic search of a person’s presence in a surveillance
video. Two fundamental problems are critical for
person re-identification, feature representation and metric
learning. An effective feature representation should be robust
to illumination and viewpoint changes, and a discriminant
metric should be learned to match various person images.
In this paper, we propose an effective feature representation
called Local Maximal Occurrence (LOMO), and
a subspace and metric learning method called Cross-view
Quadratic Discriminant Analysis (XQDA). The LOMO feature
analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation
against viewpoint changes. Besides, to handle illumination
variations, we apply the Retinex transform and a
scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional
subspace by cross-vi
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File list (Check if you may need any files):
LOMO_XQDA\bin\Retinex.mexa64
.........\...\Retinex.mexglx
.........\...\Retinex.mexw32
.........\...\Retinex.mexw64
.........\code\Demo_LOMO.m
.........\....\Demo_XQDA.m
.........\....\EvalCMC.m
.........\....\LOMO.m
.........\....\MahDist.m
.........\....\SILTP.m
.........\....\XQDA.m
.........\images\000_45_a.bmp
.........\......\000_45_b.bmp
.........\Liao-CVPR15-LOMO-XQDA.pdf
.........\LICENSE
.........\README.txt
.........\results\cuhk01_lomo_xqda.mat
.........\.......\cuhk03_detected_lomo_xqda.mat
.........\.......\cuhk03_labeled_lomo_xqda.mat
.........\.......\qmul_grid_lomo_xqda.mat
.........\.......\qmul_grid_lomo_xqda_camera-network.mat
.........\.......\viper_lomo_xqda.mat
.........\bin
.........\code
.........\data
.........\images
.........\results
LOMO_XQDA
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