基于双向参考索引的大规模人体运动数据库的检索

因为运动特征数据的高维复杂性,采用非线性的Isomap流形学习的降维算法来对运动特征数据进行降维,为了能让Isomap方法处理训练数据集之外的数据,通过学习主成分特征核函数逼近降维结果,以扩展传统Isomap的局限性.在运动数据降维之后,为大规模运动捕获数据库建立一种双向参考索引(DRI),在检索过程中索引用来排除绝大部分与查询例子无关的运动数据,这样运动检索中的相似度的计算通过索引被缩小到一个小范围候选数据集合中,避免了大量不必要的匹配开销,从而提高了检索的效率.

Motion Retrieval Based on Large-Scale 3D Human Motion Database by Double-Reference Index

In this paper, a novel approach is presented for motion retrieval based on double-reference index that reduces the number of costly distance computations for similarity measure. In order to retrieve motion data accurately, the features about joint positions, angles and velocities are extracted to represent motion data. Since these original features of motion clips lie in high-dimensional space and on a high-dimensional manifold, which is highly contorted, the Isomap nonlinear dimension reduction is used to ...