Person re-identification, aiming to identify images of the same person from various cameras configured in difference places, has attracted plenty of attention in the multimedia community. In person re-identification procedure, choosing a proper distance metric is a crucial aspect . Traditional methods always utilize a uniform learned metric, which ignored specific constraints given by this re-identification task that the learned metric is highly prone to over-fitting , and each person holding their unique characteristic brings inconsistency. Therefore, it is obviously inappropriate to merely employ a uniform metric. In this paper, we propose a data-driven metric adaptation method to improve the uniform metric. The key novelty of the approach is that we re-exploits the training data with cross-view consistency to adaptively adjust the metric. Experiments conducted on two standard data sets have validated the effectiveness of the proposed method with a significant improvement over baseline methods.
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- Person Re-identification Using Data-Driven Metric Adaptation
- Springer International Publishing