Person re-identification (PRe-ID) is a computer vision issue, that has been a
fertile research area in the last few years. It aims to identify persons across
different non-overlapping camera views. In this paper, We propose a novel
PRe-ID system that combines tensor feature representation and multilinear
subspace learning. Our method exploits the power of pre-trained Convolutional
Neural Networks (CNNs) as a strong deep feature extractor, along with two
complementary descriptors, Local Maximal Occurrence (LOMO) and Gaussian Of
Gaussian (GOG). Then, Tensor-based Cross-View Quadratic Discriminant Analysis
(TXQDA) is used to learn a discriminative subspace that enhances the
separability between different individuals. Mahalanobis distance is used to
match and similarity computation between query and gallery samples. Finally, we
evaluate our approach by conducting experiments on three datasets VIPeR, GRID,
and PRID450s.

本文提出了一个新颖的人物再识别系统，它结合了张量特征表示和多线性子空间学习。该方法利用预训练的卷积神经网络作为强大的深度特征提取器，结合局部最大出现（LOMO）和高斯特征（GOG）两种互补描述符。然后，使用基于张量的跨视图二次判别分析（TXQDA）来学习增强不同个体之间可分性的子空间。马氏距离用于查询样本和库样本之间的匹配和相似度计算。最后，我们通过在 VIPeR、GRID 和 PRID450s 三个数据集上进行实验来评估我们的方法。

推进人员识别：基于张量的特征融合和多线性子空间学习

Advancing Person Re-Identification: Tensor-based Feature Fusion and  Multilinear Subspace Learning

In this paper, we present a novel person reidentification (PRe-ID) system
that based on tensor feature representation and multilinear subspace learning.
Our approach utilizes pretrained CNNs for high-level feature extraction, along
with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG )
descriptors. Additionally, Cross-View Quadratic Discriminant Analysis (TXQDA)
algorithm is used for multilinear subspace learning, which models the data in a
tensor framework to enhance discriminative capabilities. Similarity measure
based on Mahalanobis distance is used for matching between training and test
pedestrian images. Experimental evaluations on VIPeR and PRID450s datasets
demonstrate the effectiveness of our method.

本文提出了一种基于张量特征表示和多线性子空间学习的新型个人再识别（PRe-ID）系统，该方法利用预训练的 CNN 进行高级特征提取，结合局部极大出现（LOMO）和高斯之高斯（GOG）描述符。此外，使用跨视图二次判别分析（TXQDA）算法进行多线性子空间学习，在张量框架中对数据进行建模以增强区分能力。基于马氏距离的相似度度量用于训练和测试行人图像之间的匹配。对 VIPeR 和 PRID450s 数据集进行的实验评估证明了我们方法的有效性。