  Label estimation is an important component in an unsupervised person
re-identification (re-ID) system. This paper focuses on cross-camera label
estimation, which can be subsequently used in feature learning to learn robust
re-ID models. Specifically, we propose to construct a graph for samples in each
camera, and then graph matching scheme is introduced for cross-camera labeling
association. While labels directly output from existing graph matching methods
may be noisy and inaccurate due to significant cross-camera variations, this
paper proposes a dynamic graph matching (DGM) method. DGM iteratively updates
the image graph and the label estimation process by learning a better feature
space with intermediate estimated labels. DGM is advantageous in two aspects:
1) the accuracy of estimated labels is improved significantly with the
iterations; 2) DGM is robust to noisy initial training data. Extensive
experiments conducted on three benchmarks including the large-scale MARS
dataset show that DGM yields competitive performance to fully supervised
baselines, and outperforms competing unsupervised learning methods.
