Scalable Person Re-identification on Supervised Smoothed Manifold

Song Bai, Xiang Bai, Qi Tian
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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
more » ... conventional 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.
doi:10.1109/cvpr.2017.358 dblp:conf/cvpr/BaiBT17 fatcat:moybzs6nwvc4nbauhbjkshnoqm