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3D human posture segmentation by spectral clustering with surface normal constraint
Cheng, Jun Qiao, Maoying Bian, Wei Tao, Dacheng
Cheng, Jun
Qiao, Maoying
Bian, Wei
Tao, Dacheng
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.
Keywords
constrained spectral clustering, 3D human posture segmentation
Date
2011
Type
Journal article
Journal
Signal Processing
Book
Volume
91
Issue
9
Page Range
2204-2212
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
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Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled