Abstract
In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a {\em constrained clustering optimization} problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale Resnet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.
Abstract (translated)
在这项工作中，我们提出了一个端到端的约束集群方案来解决人的重新识别问题。深度神经网络（DNN）最近已被证明是有效的人重新识别任务。特别是，通过端到端的方式传播库图像之间的相似性，而不是仅仅利用探针库相似性，已经证明能够有效地产生强大的探针库相似性。但现有的方法并没有将探针图像作为约束条件，在相似扩散过程中容易产生噪声传播。为了克服这个问题，我们提出了一个有趣的方案，将人的图像检索问题看作是一个em约束的聚类优化问题，称为深约束优势集（DCD）。在给定一个探针和图库图像的情况下，我们将人的识别问题重新表述为寻找一个受约束的簇，其中探针图像作为一个约束（种子），每个簇对应一组与同一个人对应的图像。通过以端到端的方式优化受约束的集群，我们自然地利用了与给定的人图像对应的一组图像的上下文知识。我们通过将一个辅助网络与使用多尺度Resnet的DCD集成，进一步提高了性能。为了验证该方法的有效性，我们在多个基准数据集上进行了实验，结果表明该方法优于最新的方法。
URL
https://arxiv.org/abs/1904.11397
