A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification

• URL: http://arxiv.org/abs/2509.04050v1
• Date: Thu, 04 Sep 2025 09:29:25 GMT
• Title: A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification
• Authors: Quang-Huy Che, Le-Chuong Nguyen, Gia-Nghia Tran, Dinh-Duy Phan, Vinh-Tiep Nguyen,
• Abstract summary: We present an efficient re-ranking method that generates multi-view features by aggregating neighbors' features.<n>We evaluate our method on the person re-identification datasets Market1501, MSMT17, and Occluded-DukeMTMC.
• Score: 2.1204495827342438
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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