Semi-supervised Mesh Segmentation and Labeling

Recently, approaches have been put forward that focus on the recognition of mesh semantic meanings. These methods usually need prior knowledge learned from training dataset, but when the size of the training dataset is small, or the meshes are too complex, the segmentation performance will be greatl...

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Veröffentlicht in:Computer graphics forum Jg. 31; H. 7; S. 2241 - 2248
Hauptverfasser: Lv, Jiajun, Chen, Xinlei, Huang, Jin, Bao, Hujun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford, UK Blackwell Publishing Ltd 01.09.2012
Schlagworte:
Analysis
Cost engineering
Entropy
I.3.5 [Computer Graphics]: Computer Graphics-Computational Geometry and Object Modeling
Image processing systems
Labels
Mathematical analysis
Mathematical models
Segmentation
Semantics
Studies
Training
ISSN:0167-7055, 1467-8659
Online-Zugang:Volltext
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Zusammenfassung:Recently, approaches have been put forward that focus on the recognition of mesh semantic meanings. These methods usually need prior knowledge learned from training dataset, but when the size of the training dataset is small, or the meshes are too complex, the segmentation performance will be greatly effected. This paper introduces an approach to the semantic mesh segmentation and labeling which incorporates knowledge imparted by both segmented, labeled meshes, and unsegmented, unlabeled meshes. A Conditional Random Fields (CRF) based objective function measuring the consistency of labels and faces, labels of neighbouring faces is proposed. To implant the information from the unlabeled meshes, we add an unlabeled conditional entropy into the objective function. With the entropy, the objective function is not convex and hard to optimize, so we modify the Virtual Evidence Boosting (VEB) to solve the semi‐supervised problem efficiently. Our approach yields better results than those methods which only use limited labeled meshes, especially when many unlabeled meshes exist. The approach reduces the overall system cost as well as the human labelling cost required during training. We also show that combining knowledge from labeled and unlabeled meshes outperforms using either type of meshes alone.
Bibliographie:ark:/67375/WNG-1CX1HJ3K-P
istex:668245ECE28CB848A0D7B64C45CEAA24BA88305B
ArticleID:CGF3217
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2012.03217.x