3D human posture segmentation by spectral clustering with surface normal constraint

Authors:
Jun Cheng;Maoying Qiao;Wei Bian;Dacheng Tao
Affiliations:
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China and The Chinese University of Hong Kong, Hong Kong, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China and The Chinese University of Hong Kong, Hong Kong, China;Centre for Quantum Computation and Intelligence Systems, University of Technology, Sydney, NSW 2007, Australia;Centre for Quantum Computation and Intelligence Systems, University of Technology, Sydney, NSW 2007, Australia
Venue:
Signal Processing
Year:
2011

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Abstract

In this paper, we propose a new algorithm for partitioning human posture represented by 3D point clouds sampled from the surface of human body. The algorithm is formed as a constrained extension of the recently developed segmentation method, spectral clustering (SC). Two folds of merits are offered by the algorithm: (1) as a nonlinear method, it is able to deal with the situation that data (point cloud) are sampled from a manifold (the surface of human body) rather than the embedded entire 3D space; (2) by using constraints, it facilitates the integration of multiple similarities for human posture partitioning, and it also helps to reduce the limitations of spectral clustering. We show that the constrained spectral clustering (CSC) still can be solved by generalized eigen-decomposition. Experimental results confirm the effectiveness of the proposed algorithm.