Scalable Person Re-Identification on Supervised Smoothed Manifold

Song Bai, Xiang Bai, Qi Tian; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2530-2539

Abstract


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 arises 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 unconventional manifold-preserving algorithm is proposed, which can 1) make 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.

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[bibtex]
@InProceedings{Bai_2017_CVPR,
author = {Bai, Song and Bai, Xiang and Tian, Qi},
title = {Scalable Person Re-Identification on Supervised Smoothed Manifold},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}