Person re-identification plays an important role in many safety-critical applications. Existing works mainly focus on extracting patch-level features or learning distance metrics. However, the representation power of extracted features might be limited, due to the various viewing conditions of pedestrian images in complex real-world scenarios. To improve the representation power of features, we learn discriminative and robust representations via dictionary learning in this paper. First, we propose a Cross-view Dictionary Learning (CDL) model, which is a general solution to the multi-view learning problem. Inspired by the dictionary learning based domain adaptation, CDL learns a pair of dictionaries from two views. In particular, CDL adopts a projective learning strategy, which is more efficient than the l1l_1 optimization in traditional dictionary learning. Second, we propose a Cross-view Multi-level Dictionary Learning (CMDL) approach based on CDL. CMDL contains dictionary learning models at different representation levels, including image-level, horizontal part-level, and patch-level. The proposed models take advantages of the view-consistency information, and adaptively learn pairs of dictionaries to generate robust and compact representations for pedestrian images. Third, we incorporate a discriminative regularization term to CMDL, and propose a CMDL-Dis approach which learns pairs of discriminative dictionaries in image-level and part-level. We devise efficient optimization algorithms to solve the proposed models. Finally, a fusion strategy is utilized to generate the similarity scores for test images. Experiments on the public VIPeR, CUHK Campus, iLIDS, GRID and PRID450S datasets show that our approach achieves the state-of-the-art performance.

中文翻译：

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通过跨视图多级字典学习进行人员重新识别


人员重新识别在许多安全关键应用中发挥着重要作用。现有的工作主要集中在提取补丁级特征或学习距离度量。然而，由于复杂的现实场景中行人图像的观看条件不同，提取的特征的表示能力可能受到限制。为了提高特征的表示能力，我们在本文中通过字典学习来学习判别性和鲁棒性的表示。首先，我们提出了跨视图字典学习（CDL）模型，它是多视图学习问题的通用解决方案。受基于领域适应的字典学习的启发，CDL 从两个视图学习一对字典。特别是，CDL采用投影学习策略，比传统字典学习中的l1l_1优化更加高效。其次，我们提出了一种基于 CDL 的跨视图多级字典学习（CMDL）方法。 CMDL 包含不同表示级别的字典学习模型，包括图像级别、水平部分级别和补丁级别。所提出的模型利用视图一致性信息，并自适应地学习字典对来生成行人图像的鲁棒且紧凑的表示。第三，我们将判别式正则化项融入到 CMDL 中，并提出了一种 CMDL-Dis 方法，该方法可以学习图像级和部分级的判别式字典对。我们设计了有效的优化算法来解决所提出的模型。最后，利用融合策略生成测试图像的相似度分数。在公共 VIPeR、CUHK Campus、iLIDS、GRID 和 PRID450S 数据集上的实验表明，我们的方法实现了最先进的性能。