Indexing and retrieval of human motion data by a hierarchical tree

For the convenient reuse of large-scale 3D motion capture data, browsing and searching methods for the data should be explored. In this paper, an efficient indexing and retrieval approach for human motion data is presented based on a novel similarity metric. We divide the human character model into three partitions to reduce the spatial complexity and measure the temporal similarity of each partition by self-organizing map and Smith–Waterman algorithm. The overall similarity between two motion clips can be achieved by integrating the similarities of the separate body partitions. Then the hierarchical clustering method is implemented, which can not only cluster the motion data accurately, but also discover the relationships between different motion types by a binary tree structure. With our typical cluster locating algorithm and motion motif mining method, fast and accurate retrieval can be performed. The experiment results show the effectiveness of our approach. CR Categories: I.3....

• Type: Conference
• Year: 2009
• Where: VRST
• Authors: Shuangyuan Wu, Zhaoqi Wang, Shihong Xia
• Keywords: Human Motion Data, Motion Capture Data, Motion Data, Software Engineering, VRST 2009