Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
Analysis of Japanese dance movements using motion capture system
Systems and Computers in Japan
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Content-based retrieval for human motion data
Journal of Visual Communication and Image Representation
3D human motion retrieval based on ISOMAP dimension reduction
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Hi-index | 0.00 |
A similarity retrieval of motion capture data has received substantial attention in recent years. In this paper, we focus on feature extraction and quick filtering methods in the similarity retrieval system. A representation of motion capture data is joint angles, which can distinguish different human body poses. We propose a new technique for dimensionality reduction based the average and variance of joint angles. Our dimensionality reduction is simple to understand and implement. In experiments, twenty dance motion clips each of which is different in length and style, are used in the test data set with a total of 60,000 frames. The results of our quick filtering show an achievement on the recall and precision up to 100% and 70%, respectively.