A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-Identification Article Swipe
Quang-Huy Che
,
Le-Chuong Nguyen
,
Gia-Nghia Tran
,
Dinh-Duy Phan
,
Vinh-Tiep Nguyen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5220/0013176100003905
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5220/0013176100003905
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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Metadata
- Type
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- en
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- https://doi.org/10.5220/0013176100003905
- OA Status
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https://openalex.org/W4408062725Canonical identifier for this work in OpenAlex
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https://doi.org/10.5220/0013176100003905Digital Object Identifier
- Title
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A Re-Ranking Method Using K-Nearest Weighted Fusion for Person Re-IdentificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Quang-Huy Che, Le-Chuong Nguyen, Gia-Nghia Tran, Dinh-Duy Phan, Vinh-Tiep NguyenList of authors in order
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https://doi.org/10.5220/0013176100003905Publisher landing page
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2509.04050Direct OA link when available
- Concepts
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Ranking (information retrieval), Computer science, Identification (biology), Artificial intelligence, Fusion, Pattern recognition (psychology), Data mining, k-nearest neighbors algorithm, Machine learning, Botany, Linguistics, Biology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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| abstract_inverted_index.Fusion | 77 |
| abstract_inverted_index.MSMT17 | 200 |
| abstract_inverted_index.Rank@1 | 167, 195 |
| abstract_inverted_index.during | 119 |
| abstract_inverted_index.highly | 90 |
| abstract_inverted_index.issues | 37 |
| abstract_inverted_index.mainly | 24 |
| abstract_inverted_index.making | 140 |
| abstract_inverted_index.manner | 106 |
| abstract_inverted_index.method | 65, 149, 164, 189 |
| abstract_inverted_index.models | 88 |
| abstract_inverted_index.person | 1, 51, 152 |
| abstract_inverted_index.reduce | 54 |
| abstract_inverted_index.select | 99 |
| abstract_inverted_index.weight | 116 |
| abstract_inverted_index.MSMT17, | 156 |
| abstract_inverted_index.crucial | 6 |
| abstract_inverted_index.enhance | 9 |
| abstract_inverted_index.feature | 120 |
| abstract_inverted_index.focused | 25 |
| abstract_inverted_index.images, | 30 |
| abstract_inverted_index.initial | 16, 178, 185 |
| abstract_inverted_index.method. | 79 |
| abstract_inverted_index.overall | 11 |
| abstract_inverted_index.present | 49, 61 |
| abstract_inverted_index.ranking | 17, 179 |
| abstract_inverted_index.require | 134 |
| abstract_inverted_index.results | 160 |
| abstract_inverted_index.similar | 91 |
| abstract_inverted_index.studies | 22 |
| abstract_inverted_index.Previous | 21 |
| abstract_inverted_index.Weighted | 76 |
| abstract_inverted_index.accuracy | 12 |
| abstract_inverted_index.achieves | 190 |
| abstract_inverted_index.allowing | 122 |
| abstract_inverted_index.approach | 131, 206 |
| abstract_inverted_index.changes, | 42 |
| abstract_inverted_index.compared | 182, 213 |
| abstract_inverted_index.datasets | 154 |
| abstract_inverted_index.evaluate | 147 |
| abstract_inverted_index.explores | 114 |
| abstract_inverted_index.features | 27, 47, 69, 73, 84, 102 |
| abstract_inverted_index.generate | 108 |
| abstract_inverted_index.identify | 125 |
| abstract_inverted_index.improves | 166 |
| abstract_inverted_index.methods. | 217 |
| abstract_inverted_index.refining | 14 |
| abstract_inverted_index.results, | 186 |
| abstract_inverted_index.results. | 20, 180 |
| abstract_inverted_index.K-nearest | 75 |
| abstract_inverted_index.datasets. | 145 |
| abstract_inverted_index.datasets: | 199 |
| abstract_inverted_index.effective | 127 |
| abstract_inverted_index.efficient | 63 |
| abstract_inverted_index.extracted | 85 |
| abstract_inverted_index.features. | 110 |
| abstract_inverted_index.generates | 67 |
| abstract_inverted_index.identity. | 96 |
| abstract_inverted_index.retrieved | 19 |
| abstract_inverted_index.selection | 117 |
| abstract_inverted_index.strategy. | 128 |
| abstract_inverted_index.viewpoint | 41 |
| abstract_inverted_index.9.8%/22.0% | 193 |
| abstract_inverted_index.applicable | 142 |
| abstract_inverted_index.candidates | 175 |
| abstract_inverted_index.efficiency | 212 |
| abstract_inverted_index.multi-view | 46, 68, 109 |
| abstract_inverted_index.neighbors' | 72 |
| abstract_inverted_index.re-ranking | 3, 64, 130, 171, 188, 216 |
| abstract_inverted_index.strategies | 118 |
| abstract_inverted_index.variation, | 40 |
| abstract_inverted_index.Market1501, | 155 |
| abstract_inverted_index.aggregating | 71 |
| abstract_inverted_index.challenging | 198 |
| abstract_inverted_index.fine-tuning | 136 |
| abstract_inverted_index.hypothesize | 82 |
| abstract_inverted_index.large-scale | 144 |
| abstract_inverted_index.neighboring | 101 |
| abstract_inverted_index.occlusions. | 44 |
| abstract_inverted_index.single-view | 29 |
| abstract_inverted_index.substantial | 208 |
| abstract_inverted_index.Furthermore, | 204 |
| abstract_inverted_index.aggregation, | 121 |
| abstract_inverted_index.annotations, | 139 |
| abstract_inverted_index.demonstrates | 207 |
| abstract_inverted_index.enhancements | 209 |
| abstract_inverted_index.improvements | 191 |
| abstract_inverted_index.representing | 93 |
| abstract_inverted_index.unsupervised | 105 |
| abstract_inverted_index.Additionally, | 111 |
| abstract_inverted_index.Specifically, | 80, 181 |
| abstract_inverted_index.computational | 211 |
| abstract_inverted_index.respectively. | 203 |
| abstract_inverted_index.significantly | 165 |
| abstract_inverted_index.re-identification | 87, 153 |
| abstract_inverted_index.Occluded-DukeMTMC, | 202 |
| abstract_inverted_index.Occluded-DukeMTMC. | 158 |
| abstract_inverted_index.re-identification, | 2 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.05002812 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |