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Human body shape prediction and analysis using predictive clustering tree

 
 
Affiliation:
NRC Institute for Information Technology; National Research Council Canada
Language:
English
Type:
Conference publication
Conference:
International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT 2011): May 16-19, 2011, Hangzhou, China
Proceedings
Title:
2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)
Date:
2011
Pages :
196-203
NPArC #:
17364198
Keywords:
Predictive modeling; digital human modeling; predictive clustering tree; demographic attributes
Program(s):
3D Imaging, Modeling and Visualization; Imagerie 3D, modélisation et visualisation
Project(s):
Digital Human Modeling; Modélisation humaine numérique
Group(s):
Visual Information Technology; Technologie de l'information visuelle
Abstract:
Predictive modeling aims at constructing models that predict a target property of an object based on its descriptions. In digital human modeling, it can be applied to predicting human body shape from images, measurements, or descriptive features. While images and measurements can be converted to numerical values, it is difficult to assign numerical values to descriptive features and therefore regression based methods cannot be applied. In this work, we propose to use Predictive Clustering Trees (PCT) to predict human body shapes from demographic information. We build PCTs using a dataset of demographic attributes and body shape descriptors. We demonstrate empirically that the PCT-based method has similar predicting power as the numerical approaches using body measurements. The PCTs also reveal interesting structures of the training dataset and provide interpretations of the body shape variations from the perspective of the demographic attributes.
 
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