Classification and Retrieval of CAD Three Dimensional Models Based on Neural Network

DOI

10.2991/pntim-19.2019.88How to use a DOI?

Keywords

Neural Network; Classification of CAD 3D Model; Retrieval of CAD 3D

Abstract

Several neural networks corresponding to feature space in the paper were formed by Boosting method variant and RBF neural network based on particle swarm optimization (PSO), and these neural networks were integrated, so that the classification information of CAD three dimensional (3D) models was given. In the retrieval of CAD 3D model, the distance of the output results for the classifier and the distance for the feature space were weighted to calculate, which not only considered the difference of between the model's content and features, at the same time, and appended classification information parameters, but also took into account the semantic classified information of model. The experimental results showed that the classification method based on neural network ensemble could effectively improve the classification accuracy of CAD 3D model as well as consider the distance between models in feature space and the distance between models at semantic classification level, so that the 3D CAD model retrieval could be greatly improved accuracy.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhao Mingxia
PY  - 2019/11
DA  - 2019/11
TI  - Classification and Retrieval of CAD Three Dimensional Models Based on Neural Network
BT  - Proceedings of the 2019 International Conference on Precision Machining, Non-Traditional Machining and Intelligent Manufacturing (PNTIM 2019)
PB  - Atlantis Press
SP  - 430
EP  - 435
SN  - 2589-4943
UR  - https://doi.org/10.2991/pntim-19.2019.88
DO  - 10.2991/pntim-19.2019.88
ID  - Mingxia2019/11
ER  -