Motion Recognition based on Manifold Learning Spectral Clustering
نویسندگان
چکیده
With the emergence of numerous 3D human motion capture databases, the effective analysis and handling of human motion data have become a major challenge so that the use of motion capture databases can be maximized. To reduce the high-dimensional complexity of data, a type of geometrical feature based on 2D geometrical space law is first extracted from human motion for the application of motion data into a low-dimensional subspace. With the aim of achieving a low-dimensional feature, identification and classification in different motions are then conducted through spectral clustering based on manifold learning to realize the automatic identification and retrieval of 3D human motion.
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