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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Online 3D Shape Segmentation by Blended Learning
verfasst von : Feiqian Zhang, Zhengxing Sun, Mofei Song, Xufeng Lang
Erschienen in: MultiMedia Modeling
Verlag: Springer International Publishing
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This paper presents a novel online 3D shape segmentation framework, which blend two learning methods together: unsupervised clustering based method, and supervised progressive learning method. The features of this method lie in four aspects. Firstly, we use weighted online learning to train a segmentation model to achieve the blended learning framework. Secondly, we perform co-segmentation based on unsupervised clustering to analyze the shape set, and initialize this segmentation model. Thirdly, based on this segmentation model, users can segment new shapes by using supervised progressive learning method. And this segmentation model can also be incrementally updated by weighted online learning during the progressive segmentation. Finally, the segmentation of shapes in the initial set can be corrected based on the updated segmentation model. Experimental results demonstrate the effectiveness of our approach.