  Despite the promising progress made in recent years, person re-identification
remains a challenging task due to complex variations in human appearances from
different camera views. This paper presents a logistic discriminant metric
learning method for this challenging problem. Different with most existing
metric learning algorithms, it exploits both original data and auxiliary data
during training, which is motivated by the new machine learning paradigm -
Learning Using Privileged Information. Such privileged information is a kind of
auxiliary knowledge which is only available during training. Our goal is to
learn an optimal distance function by constructing a locally adaptive decision
rule with the help of privileged information. We jointly learn two distance
metrics by minimizing the empirical loss penalizing the difference between the
distance in the original space and that in the privileged space. In our
setting, the distance in the privileged space functions as a local decision
threshold, which guides the decision making in the original space like a
teacher. The metric learned from the original space is used to compute the
distance between a probe image and a gallery image during testing. In addition,
we extend the proposed approach to a multi-view setting which is able to
explore the complementation of multiple feature representations. In the
multi-view setting, multiple metrics corresponding to different original
features are jointly learned, guided by the same privileged information.
Besides, an effective iterative optimization scheme is introduced to
simultaneously optimize the metrics and the assigned metric weights. Experiment
results on several widely-used datasets demonstrate that the proposed approach
is superior to global decision threshold based methods and outperforms most
state-of-the-art results.
