A New Index Method for Large Motion Capture Data

In this paper, our goal is to develop an efficient index method based on dimenisonality reduction of motion capture data. Due to high dimensionality of motion´s features, nonlinear PCA and Radial Basis Function(RBF) neural network for dimensionality reduction are used. Then reference index is built based on selecting a small set of representative motion clips in the database. So we can get candidate set by abandoning most unrelated motion clips to reduce the number of costly similarity measure significantly. Experiment results show that our methods are effective for motion data retrieval in large-scale motion databases.