Relative Distance Metric Leaning Based on Clustering Centralization and Projection Vectors Learning for Person Re-Identification
Existing projection-based person re-identification methods usually suffer from long time training, high dimension of projection matrix, and low matching rate. In addition, the intra-class instances may be much less than the inter-class instances when a training data set is built. To solve these prob...
Main Authors: | Tongguang Ni, Zongyuan Ding, Fuhua Chen, Hongyuan Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8263222/ |
Similar Items
-
Modeling Unknown Class Centers for Metric Learning on Person Re-Identification
by: Yuan Yuan, et al.
Published: (2018-01-01) -
A Few-Shot Learning Method Using Feature Reparameterization and Dual-Distance Metric Learning for Object Re-Identification
by: Sheng-Hung Fan, et al.
Published: (2021-01-01) -
Person Re-identification Based on Kernel Local Fisher Discriminant Analysis and Mahalanobis Distance Learning
by: He, Qiangsen
Published: (2017) -
Zero-Shot Classification Based on Word Vector Enhancement and Distance Metric Learning
by: Ji Zhang, et al.
Published: (2020-01-01) -
Person Re-Identification by Pose Invariant Deep Metric Learning With Improved Triplet Loss
by: Min Chen, et al.
Published: (2018-01-01)