# Enhancing Person Re-identification in a Self-trained Subspace

arXiv: https://arxiv.org/abs/1704.06020 · HTML: https://mlsanity.com/paper/1704.06020/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2017.

## Built on by (most cited first, 6 of 6)

- [Deep Learning-based Occluded Person Re-identification: A Survey](https://mlsanity.com/paper/2207.14452/) (2022)
- [BiC-Net: Learning Efficient Spatio-Temporal Relation for Text-Video Retrieval](https://mlsanity.com/paper/2110.15609/) (2021)
- [Towards Generalizable Person Re-identification with a Bi-stream Generative Model](https://mlsanity.com/paper/2206.09362/) (2022)
- [Self-Paced Uncertainty Estimation for One-shot Person Re-Identification](https://mlsanity.com/paper/2104.09152/) (2021)
- [Context Sensing Attention Network for Video-based Person Re-identification](https://mlsanity.com/paper/2207.02631/) (2022)
- [Dual-stream Feature Augmentation for Domain Generalization](https://mlsanity.com/paper/2409.04699/) (2024)

## Abstract (arXiv)

Despite the promising progress made in recent years, person re-identification (re-ID) remains a challenging task due to the complex variations in human appearances from different camera views. For this challenging problem, a large variety of algorithms have been developed in the fully-supervised setting, requiring access to a large amount of labeled training data. However, the main bottleneck for fully-supervised re-ID is the limited availability of labeled training samples. To address this problem, in this paper, we propose a self-trained subspace learning paradigm for person re-ID which effectively utilizes both labeled and unlabeled data to learn a discriminative subspace where person images across disjoint camera views can be easily matched. The proposed approach first constructs pseudo pairwise relationships among unlabeled persons using the k-nearest neighbors algorithm. Then, with the pseudo pairwise relationships, the unlabeled samples can be easily combined with the labeled samples to learn a discriminative projection by solving an eigenvalue problem. In addition, we refine the pseudo pairwise relationships iteratively, which further improves the learning performance. A multi-kernel embedding strategy is also incorporated into the proposed approach to cope with the non-linearity in person's appearance and explore the complementation of multiple kernels. In this way, the performance of person re-ID can be greatly enhanced when training data are insufficient. Experimental results on six widely-used datasets demonstrate the effectiveness of our approach and its performance can be comparable to the reported results of most state-of-the-art fully-supervised methods while using much fewer labeled data.