论文标题
对人重新识别属性的实证研究
An Empirical Study of Person Re-Identification with Attributes
论文作者
论文摘要
人重新识别旨在从图像收集中识别一个人，鉴于该人作为查询的一个图像。但是，有很多现实生活中的场景，我们可能没有先验的查询图像库，因此必须依靠其他模式的信息。在本文中，提出了一种基于属性的方法，其中感兴趣的人（POI）用一组可视属性来描述，这些属性用于执行搜索。我们比较多种算法，并分析属性的质量如何影响性能。虽然先前的工作主要依赖于专家注释的高精度属性，但我们进行了一项人为主体的研究，并揭示了人类观察者无法一致地描述某些视觉属性，从而使它们在实际应用中的可靠性降低。一个关键的结论是，非专家属性而不是专家注册的属性所实现的性能是对人重新识别基于属性的方法的现状的更忠实的指标。
Person re-identification aims to identify a person from an image collection, given one image of that person as the query. There is, however, a plethora of real-life scenarios where we may not have a priori library of query images and therefore must rely on information from other modalities. In this paper, an attribute-based approach is proposed where the person of interest (POI) is described by a set of visual attributes, which are used to perform the search. We compare multiple algorithms and analyze how the quality of attributes impacts the performance. While prior work mostly relies on high precision attributes annotated by experts, we conduct a human-subject study and reveal that certain visual attributes could not be consistently described by human observers, making them less reliable in real applications. A key conclusion is that the performance achieved by non-expert attributes, instead of expert-annotated ones, is a more faithful indicator of the status quo of attribute-based approaches for person re-identification.
