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中文摘要：
运动数据的行为分割是运动捕获过程中非常重要的一环.针对现有分割方法的不足,提出了一种可用于行为分割的运动数据表示方法,并基于该表示实现了数据的行为分割.运动数据经过谱聚类（spectral clustering）、时序恢复和最大值滤波法（max filtering）后生成一个字符串,该字符串称为运动串,然后采用后缀树（suffix tree）分析运动串,提取出所有静态子串和周期子串,对这些子串进行行为标注,从而实现运动数据的行为分割.实验表明,基于运动串的分割具有较好的鲁棒性和分割效果.
英文摘要：
Currently, motion data are often stored in small clips for being used in animations and games. So the behavior segmentation of motion data is a key problem in the process of motion capture. In order to segment the motion data into small clips, a new symbolic representation of motion capture data is introduced and a behavior segmentation approach based on the representation is explored. The high dimensional motion capture data are first mapped on a low dimensional space, based on spectral clustering and sliding-window distance extending weighted quaternion distances. Then the low dimensional data can be represented by a character string, called motion string （MS）, and by temporal reverting and max filtering. Because MS converts motion data into a character string, lots of string analysis methods can be used for motion processing. In addition to motion segmentation, motion string may be widely applied in various other areas such as motion retrieval and motion compression. Suffix trees are used to segment the motion data by extracting all static substrings and periodic substrings from MS. Each substring represents a behavior segment, and the motion data are segmented into distinct behavior segments by annotating these substrings. In the experiments, MS is proved to be a powerful concept for motion segmentation, providing the good performance.
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