基于集成多示例学习决策树分析的三维运动检索

3D Motion Retrieval Based on Ensemble Multiple Instance Learning Decision Tree

随着运动捕获设备的普及,大量的运动数据可以直接得到,从而使得大规模的运动数据库的建立成为可能.在此背景下,研究以检索为核心的运动捕获数据处理技术就显得十分重要了.提出了一种对运动捕获数据中的人体的各个关节点提取一种基于三维空间变换规律的三维空间特征的方法,并基于空间运动连续性引入了关键空间的概念.针对各关节点空间特征相对保持独立的特性,用每个关节点作为索引,并通过集成学习的多示例决策树的学习方法去分析关节点对运动相似的不同影响,最终实现了一个高效的运动检索系统.

In this paper, a motion retrieval system is investigated from a multiple-instance learning view. In order to retrieve similar motion data, each human joint's motion clip is regarded as a bag, while each of its segments is regarded as an instance. First 3D temporal-spatial features and their keyspaces of each human joint are extracted. Then data driven decision trees based on ensemble multiple instance are automatically constructed to reflect the influence of each point during the comparison of motion similarity. Finally, the method of multiple instance retrieval is used to complete motion retrieval. Experiment results show that the approaches are effective for motion data retrieval.