@Article{JFBI-8-657,
author = {Mian Huang, Li Liu, Ruomei Wang, Xiaodong Fu and Lijun Liu},
title = {3D Garment Segmentation Based on Semi-supervised Learning Method},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2015},
volume = {8},
number = {4},
pages = {657--665},
abstract = {In this paper, we propose a semi-supervised learning method to simultaneous segmentation and labeling
of parts in 3D garments. The key idea in this work is to analyze 3D garments using semi-supervised
learning method which can label parts in various 3D garments. We first develop an objective function
based on Conditional Random Field (CRF) model to learn the prior knowledge of garment components
from a set of training examples. Then, we exploit an effective training method that utilizes JointBoost
classifiers based on the co-analysis for garments. And we modify the JointBoost to automatically cluster
the segmented components without requiring manual parameter tuning. The purpose of our method is
to relieve the manual segmentation and labeling of components in 3D garment collections. Finally, the
experimental results show the performance of our proposed method is effective.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00174},
url = {http://global-sci.org/intro/article_detail/jfbi/4747.html}
}