Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification
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DOI：
10.1109/cvpr.2016.144
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发表时间：
2016-06
期刊：
影响因子：
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通讯作者：
Faqiang Wang;W. Zuo;Liang Lin;D. Zhang;Lei Zhang
中科院分区：
文献类型：
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作者：
Faqiang Wang;W. Zuo;Liang Lin;D. Zhang;Lei Zhang
人的再识别通常被解决为单图像表示的匹配(SIR)或交叉图像表示的分类(CIR)。在这项工作中，我们利用这两类方法之间的联系，并提出了一种联合学习框架-使用卷积神经网络(CNN)来统一SIR和CIR。具体地说，我们的深层体系结构包含一个共享子网络和两个子网络，分别提取给定图像的SIR和给定图像对的CIR。对于每个图像(在探针组和画廊组中)，SIR子网络需要计算一次，并且CIR子网络的深度被要求最小以减少计算负担。因此，可以联合优化这两种类型的表示，从而以适中的计算代价追求更好的匹配精度。此外，通过成对比较和三元组比较目标学习的表示可以组合在一起以提高匹配性能。在CUHK03、CUHK01和Viper数据集上的实验表明，该方法与现有方法相比，具有较高的准确率。
Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.