Learning a Similarity Metric Discriminatively, with Application to Face Verification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Multiple-Shot Person Re-identification by HPE Signature
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Pedestrian recognition with a learned metric
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Multiple-shot person re-identification by chromatic and epitomic analyses
Pattern Recognition Letters
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In this paper, we propose a novel approach for multiple-shot people re-identification. To deal with the multimodal properties of the people appearance distribution, we formulate the re-identification problem as a local distance comparison problem, and introduce an energy-based loss function that measures the similarity between appearance instances by calculating the distance between corresponding subsets (with the same semantic meaning) in feature space. While the loss function favors short distances, which indicate high similarity between different appearances of people, it penalizes large distances and overlaps between subsets, which reflect low similarity between different appearances. In this way, fast people re-identification can be achieved in a robust manner against varying appearance. The performance of our approach has been evaluated by applying it to the public benchmark datasets ETHZ and CAVIAR4REID. Experimental results show significant improvements over previous reports.