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| 3D Garment Segmentation Based on Semi-supervised Learning Method |
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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 Joint- Boost 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.
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| Cite this article: |
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Mian Huang,Li Liu,Ruomei Wang, et al. 3D Garment Segmentation Based on Semi-supervised Learning Method[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(4): 657-665.
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2015, Vol. 8 
