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.

翻译：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行人重识别（PRe-ID）系统。我们的方法利用预训练的卷积神经网络进行高层特征提取，并结合局部最大出现（LOMO）和高斯之高斯（GOG）描述符。此外，采用跨视角二次判别分析（TXQDA）算法进行多线性子空间学习，该算法在张量框架下对数据进行建模以增强判别能力。基于马氏距离的相似性度量用于训练集与测试集行人图像的匹配。在VIPeR和PRID450s数据集上的实验评估证明了该方法的有效性。