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

GAO Xueyao; YAN Shaokang; ZHANG Chunxiang

doi:10.11999/JEIT230405

Volume 46 Issue 4

Apr. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(4): 1438-1447

GAO Xueyao, YAN Shaokang, ZHANG Chunxiang. 3D Model Classification Based on Shannon Entropy Representative Feature and Voting Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1438-1447. doi: 10.11999/JEIT230405

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GAO Xueyao, YAN Shaokang, ZHANG Chunxiang. 3D Model Classification Based on Shannon Entropy Representative Feature and Voting Mechanism[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1438-1447. doi: 10.11999/JEIT230405

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

doi: 10.11999/JEIT230405

School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China

Funds: The National Natural Science Foundation of China (61502124, 60903082), China Postdoctoral Science Foundation (2014M560249), Heilongjiang Provincial Natural Science Foundation of China (LH2022F031, LH2022F030, F2015041, F201420)

Received Date: 2023-05-12
Rev Recd Date: 2023-12-12

Available Online: 2023-12-20

Publish Date: 2024-04-24

Abstract

Abstract

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.
- 3D model classification,
- Attention mechanism,
- Shannon entropy representative feature,
- Voting mechanism

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References(25)

References

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