Abstract
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.
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Li, F., Huang, T., Li, L. (2007). Method of Motion Data Processing Based on Manifold Learning. In: Hui, Kc., et al. Technologies for E-Learning and Digital Entertainment. Edutainment 2007. Lecture Notes in Computer Science, vol 4469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73011-8_26
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DOI: https://doi.org/10.1007/978-3-540-73011-8_26
Publisher Name: Springer, Berlin, Heidelberg
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