TY - GEN
T1 - Method of motion data processing based on manifold learning
AU - Li, Fengxia
AU - Huang, Tianyu
AU - Li, Lijie
PY - 2007
Y1 - 2007
N2 - Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was proposed. Isomap, a classical manifold learning algorithm, was necessary to be improved and extended in this paper. A framework of motion data processing based on manifold learning was built to embed high-dimensionality data into low-dimensionality space. It simplified the motion analysis, and in the same time preserved the original motion features. In order to solve the inefficiency of processing large-scale motion data, Sample Isomap (S-Isomap) algorithm was proposed. Experiments proved that approximate embeddings of motion data computed by S-Isomap were average 10 times faster than by Isomap, while 10% frame samples were selected.
AB - Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was proposed. Isomap, a classical manifold learning algorithm, was necessary to be improved and extended in this paper. A framework of motion data processing based on manifold learning was built to embed high-dimensionality data into low-dimensionality space. It simplified the motion analysis, and in the same time preserved the original motion features. In order to solve the inefficiency of processing large-scale motion data, Sample Isomap (S-Isomap) algorithm was proposed. Experiments proved that approximate embeddings of motion data computed by S-Isomap were average 10 times faster than by Isomap, while 10% frame samples were selected.
UR - https://www.scopus.com/pages/publications/38049117444
U2 - 10.1007/978-3-540-73011-8_26
DO - 10.1007/978-3-540-73011-8_26
M3 - Conference contribution
AN - SCOPUS:38049117444
SN - 9783540730101
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 259
BT - Technologies for E-Learning and Digital Entertainment - Second International Conference, Edutainment 2007, Proceedings
PB - Springer Verlag
T2 - 2nd International Conference on Edutainment, Edutainment 2007
Y2 - 11 June 2007 through 13 June 2007
ER -