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Joint Discriminative and Metric Embedding Learning for Person Re-Identification
Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise.
A Supervised Low-Rank Method for Learning Invariant Subspaces
Sparse representation and low-rank matrix decomposition approaches have been successfully applied to several computer vision problems.
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