3D Model Classification Based on Shannon Entropy Representative Feature and Voting Mechanism

被引：0
|
作者：
Gao X. [1 ]
Yan S. [1 ]
Zhang C. [1 ]
机构：
[1] School of Computer Science and Technology, Harbin University of Science and Technology, Harbin
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 04期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
3D model classification; Attention mechanism; Shannon entropy representative feature; Voting mechanism;
D O I：
10.11999/JEIT230405
中图分类号：
学科分类号：
摘要：
At present, view-based 3D model classification has the problems of insufficient visual information for single view and redundant information for multiple views, and treating all views equally will ignore the differences between different projection angles. To solve the above problems, a 3D model classification method based on Shannon entropy representative feature and voting mechanism is proposed. Firstly, multiple angle groups are set uniformly around 3D model, and multiple view sets representing the model are obtained. In order to extract effectively deep features from view, channel attention mechanism is introduced into the feature extraction network. Secondly, based on view discriminative features output from Softmax function, Shannon entropy is used to select representative feature for avoiding redundant feature of multiple views. Finally, based on representative features from multiple angle groups, voting mechanism is used to classify 3D model. Experiments show that the classification accuracy of the proposed method on 3D model dataset ModelNet10 reaches 96.48%, and classification performance is outstanding. © 2024 Science Press. All rights reserved.
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页码：1438 / 1447
页数：9
相关论文
共 25 条
  • [1] KRIZHEVSKY A, SUTSKEVER I, HINTON G E., ImageNet classification with deep convolutional neural networks[J], Communications of the ACM, 60, 6, pp. 84-90, (2017)
  • [2] SZEGEDY C, LIU Wei, JIA Yangqing, Et al., Going deeper with convolutions[C], 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1-9, (2015)
  • [3] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, Et al., Deep residual learning for image recognition[C], 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, (2016)
  • [4] CHARLES R Q, SU Hao, MO Kaichun, Et al., PointNet: Deep learning on point sets for 3D classification and segmentation[C], 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77-85, (2017)
  • [5] QI C R, YI Li, SU Hao, Et al., PointNet++: Deep hierarchical feature learning on point sets in a metric space[C], Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5105-5114, (2017)
  • [6] SONG Yupeng, HE Fazhi, DUAN Yansong, Et al., A kernel correlation-based approach to adaptively acquire local features for learning 3D point clouds, Computer-Aided Design, 146, (2022)
  • [7] ZHANG Su, YANG Jun, 3D model classification using spatial structure information[J], Journal of Chinese Computer Systems, 42, 4, pp. 779-784, (2021)
  • [8] HASSAN R, FRAZ M M, RAJPUT A, Et al., Residual learning with annularly convolutional neural networks for classification and segmentation of 3D point clouds[J], Neurocomputing, 526, pp. 96-108, (2023)
  • [9] ZHOU Ruqin, Xixing LI, JIANG Wanshou, SCANet: A spatial and channel attention based network for partial-to-partial point cloud registration[J], Pattern Recognition Letters, 151, pp. 120-126, (2021)
  • [10] WU Zhirong, SONG Shuran, KHOSLA A, Et al., 3D ShapeNets: A deep representation for volumetric shapes[C], 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912-1920, (2015)
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