A new framework for composing vectorial semantic labels in 3D model retrieval

Langshi Chen; Biao Leng; Zhang Xiong; Chen Chen

doi:10.1117/12.910564

28 October 2011 A new framework for composing vectorial semantic labels in 3D model retrieval

Langshi Chen, Biao Leng, Zhang Xiong, Chen Chen

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.

Citation Download Citation

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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