A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-Identification

Quang-Huy Che, Quang-Huy Che, Le-Chuong Nguyen, Le-Chuong Nguyen, Gia-Nghia Tran, Gia-Nghia Tran, Dinh-Duy Phan, Dinh-Duy Phan, Vinh-Tiep Nguyen, Vinh-Tiep Nguyen

2025

Abstract

In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused on features from single-view images, which can cause view bias and issues like pose variation, viewpoint changes, and occlusions. Using multi-view features to present a person can help reduce view bias. In this work, we present an efficient re-ranking method that generates multi-view features by aggregating neighbors’ features using K-nearest Weighted Fusion (KWF) method. Specifically, we hypothesize that features extracted from re-identification models are highly similar when representing the same identity. Thus, we select K neighboring features in an unsupervised manner to generate multi-view features. Additionally, this study explores the weight selection strategies during feature aggregation, allowing us to identify an effective strategy. Our re-ranking approach does not require model fine-tuning or extra annotations, making it applicable to large-scale datasets. We evaluate our method on the person re-identification datasets Market1501, MSMT17, and Occluded-DukeMTMC. The results show that our method significantly improves Rank@1 and mAP when re-ranking the top M candidates from the initial ranking results. Specifically, compared to the initial results, our re-ranking method achieves improvements of 9.8%/22.0% in Rank@1 on the challenging datasets: MSMT17 and Occluded-DukeMTMC, respectively. Furthermore, our approach demonstrates substantial enhancements in computational efficiency compared to other re-ranking methods.

Paper Citation

in Harvard Style

Che Q., Nguyen L., Tran G., Phan D. and Nguyen V. (2025). A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-Identification. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 79-90. DOI: 10.5220/0013176100003905

in Bibtex Style

@conference{icpram25,
author={Quang-Huy Che and Le-Chuong Nguyen and Gia-Nghia Tran and Dinh-Duy Phan and Vinh-Tiep Nguyen},
title={A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-Identification},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={79-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013176100003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-Identification
SN - 978-989-758-730-6
AU - Che Q.
AU - Nguyen L.
AU - Tran G.
AU - Phan D.
AU - Nguyen V.
PY - 2025
SP - 79
EP - 90
DO - 10.5220/0013176100003905
PB - SciTePress