# Multilinear subspace learning for person re-identification based fusion of high order tensor features

arXiv: https://arxiv.org/abs/2505.15825 · HTML: https://mlsanity.com/paper/2505.15825/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 73 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 11 of 11)

- [A Discriminatively Learned CNN Embedding for Person Re-identification](https://mlsanity.com/paper/1611.05666/) (2016)
- [Deep Representation Learning with Part Loss for Person Re-Identification](https://mlsanity.com/paper/1707.00798/) (2017)
- [End-to-End Comparative Attention Networks for Person Re-identification](https://mlsanity.com/paper/1606.04404/) (2016)
- [Person Re-Identification by Camera Correlation Aware Feature Augmentation](https://mlsanity.com/paper/1703.08837/) (2017)
- [Deep Attributes Driven Multi-Camera Person Re-identification](https://mlsanity.com/paper/1605.03259/) (2016)
- [Pose-driven Attention-guided Image Generation for Person Re-Identification](https://mlsanity.com/paper/2104.13773/) (2021)
- [Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification](https://mlsanity.com/paper/2203.01723/) (2022)
- [Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption](https://mlsanity.com/paper/2203.00672/) (2022)
- [Deep Attention Aware Feature Learning for Person Re-Identification](https://mlsanity.com/paper/2003.00517/) (2020)
- [Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review](https://mlsanity.com/paper/2409.07128/) (2024)
- [High-Order Multilinear Discriminant Analysis via Order-textitn Tensor Eigendecomposition](https://mlsanity.com/paper/2205.09191/) (2022)

## Built on by (most cited first, 1 of 1)

- [Recent Advances in Transformer and Large Language Models for UAV Applications](https://mlsanity.com/paper/2508.11834/) (2025)

## Abstract (arXiv)

Video surveillance image analysis and processing is a challenging field in computer vision, with one of its most difficult tasks being Person Re-Identification (PRe-ID). PRe-ID aims to identify and track target individuals who have already been detected in a network of cameras, using a robust description of their pedestrian images. The success of recent research in person PRe-ID is largely due to effective feature extraction and representation, as well as the powerful learning of these features to reliably discriminate between pedestrian images. To this end, two powerful features, Convolutional Neural Networks (CNN) and Local Maximal Occurrence (LOMO), are modeled on multidimensional data using the proposed method, High-Dimensional Feature Fusion (HDFF). Specifically, a new tensor fusion scheme is introduced to leverage and combine these two types of features in a single tensor, even though their dimensions are not identical. To enhance the system's accuracy, we employ Tensor Cross-View Quadratic Analysis (TXQDA) for multilinear subspace learning, followed by cosine similarity for matching. TXQDA efficiently facilitates learning while reducing the high dimensionality inherent in high-order tensor data. The effectiveness of our approach is verified through experiments on three widely-used PRe-ID datasets: VIPeR, GRID, and PRID450S. Extensive experiments demonstrate that our approach outperforms recent state-of-the-art methods.