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MVCLN: Multi-View Convolutional LSTM Network for Cross-Media 3D Shape Recognition
- Source :
- IEEE Access, Vol 8, Pp 139792-139802 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Cross-media 3D model recognition is an important and challenging task in computer vision, which can be utilized in many applications such as landmark detection, image set classification, etc. In recent years, with the development of deep learning, many approaches have been proposed to handle the 3D model recognition problem. However, all of these methods focus on the structure information representation and the multi-view information fusion, and ignore the spatial and temporal information. So that it is not suitable for the cross-media 3D model recognition. In this paper, we utilize the sequence views to represent each 3D model and propose a novel Multi-view Convolutional LSTM Network (MVCLN), which utilizes the LSTM structure to extract temporal information and applies the convolutional operation to extract spatial information. More especially, the spatial and temporal information both are considered during the training process, which can effectively utilize the differences between the view's spatial information to improve the final performance. Meanwhile, we also introduce the classic attention model to define the weight of each view, which can reduce the redundant information of view's spatial information in the information fusion step. We evaluate the proposed method on the ModelNet40 for 3D model classification and retrieval task. We also construct a dataset utilizing the overlap categories of MV-RED, ShapenetCore and ModelNet to demonstrate the effectiveness of our approach for the cross-media 3D model recognition. Experimental results and comparisons with the state-of-the-art methods demonstrate that our framework can achieve superior performance.
- Subjects :
- 3D model
General Computer Science
Computer science
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Task (project management)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Set (psychology)
retrieval
Spatial analysis
Structure (mathematical logic)
Landmark
business.industry
Deep learning
010401 analytical chemistry
General Engineering
020206 networking & telecommunications
Construct (python library)
0104 chemical sciences
Convolutional LSTM
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
recognition
Focus (optics)
business
lcsh:TK1-9971
computer
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....44fe05f80836969d384a5dd5b9db5a42