Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric

• URL: http://arxiv.org/abs/2011.11506v1
• Date: Mon, 23 Nov 2020 16:11:19 GMT
• Title: Re-identification = Retrieval + Verification: Back to Essence and Forward with a New Metric
• Authors: Zheng Wang, Xin Yuan, Toshihiko Yamasaki, Yutian Lin, Xin Xu, Wenjun Zeng
• Abstract summary: We propose Genuine Open-set re-ID Metric (GOM) as a new re-identification metric. GOM balances the effect of performing retrieval and verification into a single unified metric. GOM scores excellent in aligning with human visual evaluation of re-ID performance.
• Score: 88.96593495602923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Re-identification (re-ID) is currently investigated as a closed-world image retrieval task, and evaluated by retrieval based metrics. The algorithms return ranking lists to users, but cannot tell which images are the true target. In essence, current re-ID overemphasizes the importance of retrieval but underemphasizes that of verification, \textit{i.e.}, all returned images are considered as the target. On the other hand, re-ID should also include the scenario that the query identity does not appear in the gallery. To this end, we go back to the essence of re-ID, \textit{i.e.}, a combination of retrieval and verification in an open-set setting, and put forward a new metric, namely, Genuine Open-set re-ID Metric (GOM). GOM explicitly balances the effect of performing retrieval and verification into a single unified metric. It can also be decomposed into a family of sub-metrics, enabling a clear analysis of re-ID performance. We evaluate the effectiveness of GOM on the re-ID benchmarks, showing its ability to capture important aspects of re-ID performance that have not been taken into account by established metrics so far. Furthermore, we show GOM scores excellent in aligning with human visual evaluation of re-ID performance. Related codes are available at https://github.com/YuanXinCherry/Person-reID-Evaluation

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