标题:A general re-ranking method based on metric learning for person re-identification
作者:Xu, Tongkun ;Zhao, Xin ;Hou, Jiamin ;Hao, Xinhong ;Zhang, Jiyong ;Yin, Jian
作者机构:[Xu, Tongkun ;Yin, Jian ] Shandong University, Weihai, China;[Hou, Jiamin ] China University of Mining Technology, Beijing, China;[Zhao, Xin ] Jd.com, 更多
会议名称:2020 IEEE International Conference on Multimedia and Expo, ICME 2020
会议日期:6 July 2020 through 10 July 2020
来源:Proceedings - IEEE International Conference on Multimedia and Expo
出版年:2020
卷:2020-July
DOI:10.1109/ICME46284.2020.9102887
关键词:Person re-identification; Re-ranking
摘要:When Person Re-identification is considered as a retrieval task, re-ranking becomes a critical part of improving the re-identification accuracy. Most of the existing re-ranking methods focus on k -nearest neighbors, which requires a lot of queries and memory. In this paper, we propose a Feature Relation Map based Similarity Evaluation (FRM-SE) model to tackle this problem. The Feature Relation Map is utilized to automatically mine the latent relation between the k -neighbors through convolution operation. The re-ranking distance is learned through the FRM-SE model with metric learning. Further, we optimize the existing re-ranking method to utilize the advantage of the FRM-SE model for maintaining a balance between accuracy and complexity.The proposed approach is validated on two benchmark datasets, Market1501 and CUHK03. Results show that our re-ranking method is superior to the state-of-the-art re-ranking methods. Furthermore, in the transfer learning setting, the model trained on either Market1501 or CUHK03 can achieve a comparable accuracy improvement on the DuekMTMC dataset, which validates the generalization of our SE model. © 2020 IEEE.
收录类别:EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090390799&doi=10.1109%2fICME46284.2020.9102887&partnerID=40&md5=5e921e6419f1be3ac77051699f1b2b2f
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