Most existing person re-identification algorithms either extract robust
visual features or learn discriminative metrics for person images. However, the
underlying manifold which those images reside on is rarely investigated. That
raises a problem that the learned metric is not smooth with respect to the
local geometry structure of the data manifold.
In this paper, we study person re-identification with manifold-based affinity
learning, which did not receive enough attention from this area. An
unconventional manifold-preserving algorithm is proposed, which can 1) make the
best use of supervision from training data, whose label information is given as
pairwise constraints; 2) scale up to large repositories with low on-line time
complexity; and 3) be plunged into most existing algorithms, serving as a
generic postprocessing procedure to further boost the identification
accuracies. Extensive experimental results on five popular person
re-identification benchmarks consistently demonstrate the effectiveness of our
method. Especially, on the largest CUHK03 and Market-1501, our method
outperforms the state-of-the-art alternatives by a large margin with high
efficiency, which is more appropriate for practical applications.

在人员再识别中，我们提出了基于流形保存的亲和力学习算法，该算法可以最大程度地利用监督训练数据，并且可以被插入到现有算法中作为一种通用的后处理过程，从而进一步提高识别精度。