Paper
28 October 2011 A new framework for composing vectorial semantic labels in 3D model retrieval
Langshi Chen, Biao Leng, Zhang Xiong, Chen Chen
Author Affiliations +
Proceedings Volume 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization; 820508 (2011) https://doi.org/10.1117/12.910564
Event: 2011 International Conference on Photonics, 3D-imaging, and Visualization, 2011, Guangzhou, China
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Langshi Chen, Biao Leng, Zhang Xiong, and Chen Chen "A new framework for composing vectorial semantic labels in 3D model retrieval", Proc. SPIE 8205, 2011 International Conference on Photonics, 3D-Imaging, and Visualization, 820508 (28 October 2011); https://doi.org/10.1117/12.910564
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Cited by 1 scholarly publication.
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KEYWORDS
3D modeling

Data modeling

Databases

Fuzzy logic

Performance modeling

Feature extraction

Silicon

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