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Proceedings Paper

A new framework for composing vectorial semantic labels in 3D model retrieval
Author(s): Langshi Chen; Biao Leng; Zhang Xiong; Chen Chen
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Paper Abstract

Content based 3D model Retrieval (CB3DR) is proved to be limited in performance due to the semantic gap between low-level feature distance and high-level user intention. In order to capture semantics from models, we propose a new framework which generates semantic subspaces for each category via corresponding variances of feature vectors. Then vectorial and numerical semantic labels are composed from semantic subspaces. In the end, a Laplacian Eigenmaps based manifold learning method is enhanced by these semantic labels and experiment results show an improvement in performance with respect to classical Laplacian Eigenmaps method.

Paper Details

Date Published: 29 October 2011
PDF: 6 pages
Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 820508 (29 October 2011); doi: 10.1117/12.910564
Show Author Affiliations
Langshi Chen, Beihang Univ. (China)
Biao Leng, Beihang Univ. (China)
Zhang Xiong, Beihang Univ. (China)
Chen Chen, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 8205:
2011 International Conference on Photonics, 3D-Imaging, and Visualization
Egui Zhu, Editor(s)

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