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

• URL: http://arxiv.org/abs/2505.15825v1
• Date: Fri, 09 May 2025 23:39:27 GMT
• Title: Multilinear subspace learning for person re-identification based fusion of high order tensor features
• Authors: Ammar Chouchane, Mohcene Bessaoudi, Hamza Kheddar, Abdelmalik Ouamane, Tiago Vieira, Mahmoud Hassaballah,
• Abstract summary: PRe-ID aims to identify and track target individuals who have already been detected in a network of cameras.<n>To this end, two powerful features, Conal Neural Networks (CNN) and Local Maximal Occurrence (LOMO) are modeled on multidimensional data.<n>New tensor fusion scheme is introduced to leverage and combine these two types of features in a single tensor.
• Score: 2.03240755905453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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