Human body shape prediction and analysis using predictive clustering tree

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DOIResolve DOI: http://doi.org/10.1109/3DIMPVT.2011.32
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TypeArticle
Proceedings title2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT)
ConferenceInternational Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT 2011): May 16-19, 2011, Hangzhou, China
ISBN978-0-7695-4369-7
Pages196203; # of pages: 8
SubjectPredictive modeling; digital human modeling; predictive clustering tree; demographic attributes
AbstractPredictive 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.
Publication date
PublisherIEEE Computer Society
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NPARC number17364198
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Record identifier06675f4d-086d-4ad9-9206-d27d65a563c0
Record created2011-03-22
Record modified2016-05-09
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