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2018 | OriginalPaper | Chapter

Semantic Relation Driven SVM-Based Function Recognition for 3D Shape Components

Authors : Lingling Zi, Xin Cong

Published in: Proceedings of 2017 Chinese Intelligent Automation Conference

Publisher: Springer Singapore

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Abstract

To solve the problem of automatic recognition in the presence of significant geometric and topological variations of 3D shape components, a semantic relation driven SVM-based function recognition method is proposed. Firstly, the shape segmentation scheme based on approximate convexity decomposition is proposed to decompose the shape into shape components with different semantics. Secondly, a functional semantic similarity method based on component context relations is presented to qualitatively measure semantic relations between the obtained shape components. Finally, the SVM classifier with functional semantic similarity as kernel function is constructed to achieve the task of shape recognition. Experimental results show that the proposed method could improve the accuracy of function recognition of 3D shapes, especially for shapes with large-scale deformation.

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Metadata
Title
Semantic Relation Driven SVM-Based Function Recognition for 3D Shape Components
Authors
Lingling Zi
Xin Cong
Copyright Year
2018
Publisher
Springer Singapore
Book
Proceedings of 2017 Chinese Intelligent Automation Conference

Print ISBN: 978-981-10-6444-9

Electronic ISBN: 978-981-10-6445-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-6445-6_8