Top-Push Polynomial Ranking Embedded Dictionary Learning for Enhanced Re-Id

Ying Chen, De Cheng, Zhihui Li, Andy Song

2025

Abstract

Person re-identification (Re-Id) aims to match pedestrians captured by multiple non-overlapping cameras. In this paper, we introduce a novel dictionary learning approach enhanced with a top-push polynomial ranking metric for improved Re-Id performance. A key feature of our method is the incorporation of a ranking graph Laplacian term, designed to minimize intra-class compactness and maximize inter-class dispersion. Specifically, we employ a polynomial distance function to evaluate similarity between person images and propose the Top-push Polynomial Ranking Loss (TPRL) function, which enforces a margin between positive matching pairs and their closest non-matching pairs. The TPRL is then embedded into the dictionary learning objective, enabling our method to capture essential ranking relationships among person images—a critical aspect for retrieval-focused tasks. Unlike traditional dictionary learning approaches, our method reformulates ranking constraints through a graph Laplacian, resulting in an approach that is both straightforward to implement and highly effective. Extensive experiments on four popular Re-Id benchmark datasets demonstrate that our method consistently outperforms existing approaches, highlighting its effectiveness and robustness.

Paper Citation

in Harvard Style

Chen Y., Cheng D., Li Z. and Song A. (2025). Top-Push Polynomial Ranking Embedded Dictionary Learning for Enhanced Re-Id. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 1262-1269. DOI: 10.5220/0013319900003890

in Bibtex Style

@conference{icaart25,
author={Ying Chen and De Cheng and Zhihui Li and Andy Song},
title={Top-Push Polynomial Ranking Embedded Dictionary Learning for Enhanced Re-Id},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={1262-1269},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013319900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Top-Push Polynomial Ranking Embedded Dictionary Learning for Enhanced Re-Id
SN - 978-989-758-737-5
AU - Chen Y.
AU - Cheng D.
AU - Li Z.
AU - Song A.
PY - 2025
SP - 1262
EP - 1269
DO - 10.5220/0013319900003890
PB - SciTePress