IEEE Trans Image Process. 2017 Mar;26(3):1363-1378. doi: 10.1109/TIP.2017.2651364. Epub 2017 Jan 10.
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high resolution (HR), while probe images are usually low resolution (LR) in the identification scenarios with large variation of illumination, weather, or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLDL) approach for SR person re-identification task. With the HR and LR dictionary pair and mapping matrices learned from the features of HR and LR training images, SLDL can convert the features of the LR probe images into HR features. To ensure that the converted features have favorable discriminative capability and the learned dictionaries can well characterize intrinsic feature spaces of the HR and LR images, we design a discriminant term and a low-rank regularization term for SLDL. Moreover, considering that low resolution results in different degrees of loss for different types of visual appearance features, we propose a multi-view SLDL (MVSLDL) approach, which can learn the type-specific dictionary pair and mappings for each type of feature. Experimental results on multiple publicly available data sets demonstrate the effectiveness of our proposed approaches for the SR person re-identification task.
人像重识别由于其在监控和法庭科学应用中的重要性而得到了广泛的研究。在实际应用中，在光照、天气或摄像机质量变化较大的识别场景中，图库图像通常是高分辨率（HR）的，而探针图像通常是低分辨率（LR）的。我们将这种场景下的人像重识别称为超分辨率（SR）人像重识别，它还没有得到很好的研究。在本文中，我们提出了一种用于 SR 人像重识别任务的半耦合低秩判别字典学习（SLDL）方法。通过从 HR 和 LR 训练图像的特征中学习 HR 和 LR 字典对和映射矩阵，SLDL 可以将 LR 探针图像的特征转换为 HR 特征。为了确保转换后的特征具有良好的判别能力，并且学习到的字典能够很好地描述 HR 和 LR 图像的内在特征空间，我们为 SLDL 设计了一个判别项和一个低秩正则化项。此外，考虑到低分辨率会导致不同类型的视觉外观特征不同程度的损失，我们提出了一种多视图 SLDL（MVSLDL）方法，它可以为每种类型的特征学习特定于类型的字典对和映射。在多个公开可用的数据集上的实验结果表明，我们提出的方法对于 SR 人像重识别任务是有效的。
