计算机工程与应用2019，Vol.55Issue(20)：170-176,201,8.DOI:10.3778/j.issn.1002-8331.1808-0145
迁移度量学习行人再识别算法
Transfer Metric Learning for Person Re-Identification
宋丽丽1
作者信息
- 1. 成都理工大学 工程技术学院,四川 乐山 614000
- 折叠
摘要
Abstract
Pedestrian re-recognition is a challenging task in the field of computer vision. This task focuses on the appear-ance change pattern of individuals. Due to the drastic variation of appearance feature, there is small sample problem in metric learning for person re-identification. In this paper, a transfer metric learning based method is proposed. By mini-mizing the difference between the distribution of source data and target data, the proposed method achieves the transform of metric model from source dataset to target dataset. The proposed method not only enhances the diversity of training samples which improves the discrimination of metric model, but also improves its generalization. Finally, the effectiveness and accuracy of the proposed method are verified on the VIPeR and CUHK01 datasets by the pre-training on iLIDS dataset.关键词
行人再识别/度量学习/迁移学习Key words
person re-identification/metric learning/transfer learning分类
信息技术与安全科学引用本文复制引用
宋丽丽..迁移度量学习行人再识别算法[J].计算机工程与应用,2019,55(20):170-176,201,8.
