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Enhancing Person Re-Identification through Tensor Feature Fusion

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Akram Abderraouf Gharbi, Ammar Chouchane, Mohcene Bessaoudi, Abdelmalik Ouamane, El ouanas Belabbaci

Computer Vision and Pattern Recognition · Submitted 16 Dec 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method.

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arXiv ID2312.10470 (version 1)
Pith IDIV7LQLWU, Pith's permanent record of this paper and its review
CitationAbdelmalik Ouamane, Akram Abderraouf Gharbi, Ammar Chouchane, El Ouanas Belabbaci, Mohcene Bessaoudi. "Enhancing Person Re-Identification through Tensor Feature Fusion." arXiv:2312.10470 (2023). https://pith.science/paper/IV7LQLWU
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,268 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.04 · human participants 0.18 · controlled comparison 0.08 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.14 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we present a novel person reidentification (PRe-ID) system that based on tensor feature representation and multilinear subspace learning. contribution 1.00 0.89 0.08 asserted 1.00
s1 Our approach utilizes pretrained CNNs for high-level feature extraction, along with Local Maximal Occurrence (LOMO) and Gaussian Of Gaussian (GOG ) descriptors. method 0.99 0.78 0.03 asserted 1.00
s2 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. method 0.99 0.64 0.06 asserted 1.00
s3 Similarity measure based on Mahalanobis distance is used for matching between training and test pedestrian images. method 1.00 0.76 0.03 asserted 1.00
s4 Experimental evaluations on VIPeR and PRID450s datasets demonstrate the effectiveness of our method. result 0.96 0.95 0.74 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.91.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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.

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