论文标题
时空k-均值
Spatiotemporal k-means
论文作者
论文摘要
由于新兴传感器和跟踪移动对象的数据采集技术，时空数据越来越多地获得。时空聚类解决了在没有人类监督的情况下有效地发现对象行为的模式和趋势的需求。感兴趣的一种应用是发现移动群集，簇具有静态身份，但是它们的位置和内容可以随着时间而变化。我们提出了一种称为时空K-均值（STKM）的两相时空聚类方法，能够分析时空数据中的多尺度关系。通过优化在空间和时间上统一的目标函数，该方法可以在短时间和长时间尺度上以最小的参数调整来跟踪动态群集，并且没有后处理。我们首先提出了一个用于时空数据的理论生成模型，并在这种情况下证明了STKM的功效。然后，我们在最近开发的集体动物行为基准数据集上评估STKM，并表明STKM在低数据限制中的表现优于基线方法，这是许多新兴应用程序中的关键考虑方案。最后，我们展示了如何将STKM扩展到更复杂的机器学习任务，尤其是视频中的无监督区域检测和跟踪区域。
Spatiotemporal data is increasingly available due to emerging sensor and data acquisition technologies that track moving objects. Spatiotemporal clustering addresses the need to efficiently discover patterns and trends in moving object behavior without human supervision. One application of interest is the discovery of moving clusters, where clusters have a static identity, but their location and content can change over time. We propose a two phase spatiotemporal clustering method called spatiotemporal k-means (STkM) that is able to analyze the multi-scale relationships within spatiotemporal data. By optimizing an objective function that is unified over space and time, the method can track dynamic clusters at both short and long timescales with minimal parameter tuning and no post-processing. We begin by proposing a theoretical generating model for spatiotemporal data and prove the efficacy of STkM in this setting. We then evaluate STkM on a recently developed collective animal behavior benchmark dataset and show that STkM outperforms baseline methods in the low-data limit, which is a critical regime of consideration in many emerging applications. Finally, we showcase how STkM can be extended to more complex machine learning tasks, particularly unsupervised region of interest detection and tracking in videos.
