IEEE Trans Pattern Anal Mach Intell. 2013 Mar;35(3):582-96. doi: 10.1109/TPAMI.2012.137. Epub 2012 Jun 26.
Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.
人体运动的时间分割成合理的运动基元是理解和构建人体运动计算模型的核心。发现运动基元的挑战包括：所有可能运动组合的指数性质、人体动作时间尺度的可变性以及表示铰接运动的复杂性。我们将学习运动基元的问题作为时间聚类问题，并推导出一种称为层次对齐聚类分析（HACA）的无监督层次自下而上框架。HACA 将给定的多维时间序列分割成 m 个不相交的段，使得每个段属于 k 个簇中的一个。HACA 将核 k-均值与广义动态时间对准核相结合，用于聚类时间序列数据。此外，它为时间序列提供了一种找到低维嵌入的自然框架。HACA 可以通过坐标下降策略和动态编程进行高效优化。在运动捕捉和视频数据上的实验结果表明，HACA 可以有效地分割复杂运动，并作为可视化工具。我们还将 HACA 的性能与用于蜜蜂舞蹈数据的时间聚类的最先进算法进行了比较。HACA 代码可在线获得。
