In large-scale camera networks, label information for person re-identification is usually not available under a large amount of cameras due to expensive human labor efforts. Semi-supervised learning could be employed to train a discriminative classifier by using unlabeled data and unmatched image pairs (negatives) generated from non-overlapping camera views, but existing methods suffer from the problem of imbalanced unlabeled data. In this context, this paper proposes a novel semi-supervised region metric learning method to improve person re-identification performance under imbalanced unlabeled data. Firstly, instead of seeking for matched image pairs (positives) from the unlabeled data, we propose to estimate positive neighbors by label propagation with cross person score distribution alignment. Secondly, multiple positive regions are generated using sets of positive neighbors to learn a discriminative region-to-point metric. Experimental results demonstrate that the superiority of the proposed method over existing unsupervised, semi-supervised and person re-identification methods.

中文翻译：

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用于人员重新识别的半监督区域度量学习

在大规模相机网络中，由于昂贵的人工劳动，在大量相机下通常无法获得用于人员重新识别的标签信息。半监督学习可以通过使用未标记数据和从非重叠相机视图生成的不匹配图像对（底片）来训练判别分类器，但现有方法存在未标记数据不平衡的问题。在此背景下，本文提出了一种新的半监督区域度量学习方法，以提高不平衡未标记数据下的行人重识别性能。首先，不是从未标记的数据中寻找匹配的图像对（正数），我们建议通过标签传播和跨人得分分布对齐来估计正邻居。第二，使用正邻居集生成多个正区域以学习区分区域到点的度量。实验结果表明，所提出的方法优于现有的无监督、半监督和人员重新识别方法。