# Person Re-identification Meets Image Search

arXiv: https://arxiv.org/abs/1502.02171 · HTML: https://mlsanity.com/paper/1502.02171/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2015.

## Built on by (most cited first, 19 of 19)

- [Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification](https://mlsanity.com/paper/2107.12666/) (2021)
- [Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions](https://mlsanity.com/paper/2306.07520/) (2023)
- [SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks](https://mlsanity.com/paper/2503.06965/) (2025)
- [From Data Deluge to Data Curation: A Filtering-WoRA Paradigm for Efficient Text-based Person Search](https://mlsanity.com/paper/2404.10292/) (2024)
- [Pedestrian Attribute Recognition as Label-balanced Multi-label Learning](https://mlsanity.com/paper/2405.04858/) (2024)
- [UFO: Unified Feature Optimization](https://mlsanity.com/paper/2207.10341/) (2022)
- [Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification](https://mlsanity.com/paper/2405.17790/) (2024)
- [From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification](https://mlsanity.com/paper/2408.00096/) (2024)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [Text-based Aerial-Ground Person Retrieval](https://mlsanity.com/paper/2511.08369/) (2025)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [Motion State: A New Benchmark Multiple Object Tracking](https://mlsanity.com/paper/2312.17641/) (2023)
- [Harnessing Weak Pair Uncertainty for Text-based Person Search](https://mlsanity.com/paper/2604.08877/) (2026)
- [An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention](https://mlsanity.com/paper/2211.06001/) (2022)
- [PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method](https://mlsanity.com/paper/2303.06330/) (2023)
- [PCD-ReID: Occluded Person Re-Identification for Base Station Inspection](https://mlsanity.com/paper/2511.01546/) (2025)
- [Thinking Before Matching: A Reinforcement Reasoning Paradigm Towards General Person Re-Identification](https://mlsanity.com/paper/2604.19218/) (2026)
- [Dual-Prompt CLIP with Hybrid Visual Encoders for Occluded Person Re-Identification](https://mlsanity.com/paper/2605.19527/) (2026)
- [Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification](https://mlsanity.com/paper/2607.14821/) (2026)

## Abstract (arXiv)

For long time, person re-identification and image search are two separately studied tasks. However, for person re-identification, the effectiveness of local features and the "query-search" mode make it well posed for image search techniques. In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem. Specifically, this paper claims two major contributions. 1) By designing an unsupervised Bag-of-Words representation, we are devoted to bridging the gap between the two tasks by integrating techniques from image search in person re-identification. We show that our system sets up an effective yet efficient baseline that is amenable to further supervised/unsupervised improvements. 2) We contribute a new high quality dataset which uses DPM detector and includes a number of distractor images. Our dataset reaches closer to realistic settings, and new perspectives are provided. Compared with approaches that rely on feature-feature match, our method is faster by over two orders of magnitude. Moreover, on three datasets, we report competitive results compared with the state-of-the-art methods.