用几何原型点集实现高效轨迹检索，无需训练且速度更快。
GeoPTH: A Lightweight Approach to Category-Based Trajectory Retrieval via Geometric Prototype Trajectory Hashing
- 以代表性轨迹原型为锚点构建数据相关哈希函数
- 在准确率上媲美传统方法和先进学习模型
- 适合需要快速检索的实时轨迹应用
轨迹相似性检索是时空数据挖掘的重要任务，但现有方法存在计算成本高或训练代价大、不稳定的缺点。本文提出几何原型轨迹哈希（GeoPTH），一种轻量级、非学习型框架，用于高效类别化轨迹检索。该方法利用保留几何特征的小型点集作为原型锚点，构建数据依赖的哈希函数。哈希过程通过鲁棒的豪斯多夫度量将新轨迹映射到最近原型，计算高效。大量实验表明，GeoPTH的检索准确率与传统度量和先进学习方法相当，显著优于简单二值化学习嵌入生成的二进制码。关键的是，GeoPTH在效率上持续优于所有对比方法。本工作证明，轻量级原型中心策略在性能与效率间取得优异平衡，是一种实用且强大的替代方案。
原文摘要 · Abstract (English)
Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationally expensive, while learning-based methods suffer from substantial training costs and potential instability. This paper addresses these problems by proposing Geometric Prototype Trajectory Hashing (GeoPTH), a novel, lightweight, and non-learning framework for efficient category-based trajectory retrieval. GeoPTH constructs data-dependent hash functions by using representative trajectory prototypes, i.e., small point sets preserving geometric characteristics, as anchors. The hashing process is efficient, which involves mapping a new trajectory to its closest prototype via a robust, Hausdorff metric. Extensive experiments show that GeoPTH's retrieval accuracy is highly competitive with both traditional metrics and state-of-the-art learning methods, and it significantly outperforms binary codes generated through simple binarization of the learned embeddings. Critically, GeoPTH consistently outperforms all competitors in terms of efficiency. Our work demonstrates that a lightweight, prototype-centric approach offers a practical and powerful alternative, achieving an exceptional retrieval performance and computational efficiency.
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