Abstract The promising achievement of sparse ranking in image-based recognition gives rise to a number of development on person re-identification (Re-ID) which aims to reconstruct the probe as a linear combination of few atoms/images from an over-complete dictionary/gallery. However, most of the existing sparse ranking based Re-ID methods lack considering the geometric relationships between probe, gallery, and cross-modal images of the same person in multi-shot Re-ID. In this paper, we propose a novel joint graph regularized dictionary learning and sparse ranking method for multi-modal multi-shot person Re-ID. First, we explore the probe-based geometrical structure by enforcing the smoothness between the codings/coefficients, which refers to the multi-shot images from the same person in probe. Second, we explore the gallery-based geometrical structure among gallery images, which encourages the multi-shot images from the same person in the gallery making similar contributions while reconstructing a certain probe image. Third, we explore the cross-modal geometrical structure by enforcing the smoothness between the cross-modal images and thus extend our model for the multi-modal case. Finally, we design an APG based optimization to solve the problem. Comprehensive experiments on benchmark datasets demonstrate the superior performance of the proposed model. The code is available at https://github.com/ttaalle/Lhc .

中文翻译：

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联合图正则化字典学习和稀疏排序的多模态多镜头人重识别

摘要 在基于图像的识别中稀疏排序的有希望的成就引起了人员重新识别（Re-ID）的许多发展，其旨在将探针重建为来自过度完备字典的几个原子/图像的线性组合/画廊。然而，大多数现有的基于稀疏排序的 Re-ID 方法缺乏考虑多镜头 Re-ID 中同一个人的探针、图库和跨模态图像之间的几何关系。在本文中，我们提出了一种新的联合图正则化字典学习和稀疏排序方法，用于多模态多镜头人 Re-ID。首先，我们通过增强编码/系数之间的平滑性来探索基于探针的几何结构，这指的是来自探针中同一个人的多镜头图像。第二，我们探索了画廊图像之间基于画廊的几何结构，这鼓励画廊中同一个人的多镜头图像在重建某个探测图像的同时做出相似的贡献。第三，我们通过加强跨模态图像之间的平滑性来探索跨模态几何结构，从而扩展我们的多模态模型。最后，我们设计了一个基于 APG 的优化来解决这个问题。在基准数据集上的综合实验证明了所提出模型的优越性能。代码可在 https://github.com/ttaalle/Lhc 获得。我们通过加强跨模态图像之间的平滑性来探索跨模态几何结构，从而扩展我们的多模态模型。最后，我们设计了一个基于 APG 的优化来解决这个问题。在基准数据集上的综合实验证明了所提出模型的优越性能。代码可在 https://github.com/ttaalle/Lhc 获得。我们通过加强跨模态图像之间的平滑性来探索跨模态几何结构，从而扩展我们的多模态模型。最后，我们设计了一个基于 APG 的优化来解决这个问题。在基准数据集上的综合实验证明了所提出模型的优越性能。代码可在 https://github.com/ttaalle/Lhc 获得。