In this paper, we propose a graph correspondence transfer (GCT) approach for
person re-identification. Unlike existing methods, the GCT model formulates
person re-identification as an off-line graph matching and on-line
correspondence transferring problem. In specific, during training, the GCT
model aims to learn off-line a set of correspondence templates from positive
training pairs with various pose-pair configurations via patch-wise graph
matching. During testing, for each pair of test samples, we select a few
training pairs with the most similar pose-pair configurations as references,
and transfer the correspondences of these references to test pair for feature
distance calculation. The matching score is derived by aggregating distances
from different references. For each probe image, the gallery image with the
highest matching score is the re-identifying result. Compared to existing
algorithms, our GCT can handle spatial misalignment caused by large variations
in view angles and human poses owing to the benefits of patch-wise graph
matching. Extensive experiments on five benchmarks including VIPeR, Road,
PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over
other state-of-the-art methods.

本文提出了一种图形对应传送（GCT）方法，用于人物重新识别，该模型将人物重新识别的问题制定为离线图形匹配和在线对应传送的问题，并通过基于补丁的图形匹配从具有各种姿势对配置的正面训练对中学习一组对应关系模板。实验结果表明，与现有算法相比，GCT 模型具有更好的性能。