Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition-Reference-Cited by-同舟云学术

Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition

Published:2018-10-10 Issue:6 Volume:10 Page:908-921
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Yang Zhi-Xin^ORCID,Tang Lulu,Zhang Kun,Wong Pak Kin

Funder

Science and Technology Development Fund of Macao S.A.R

Universidade de Macau

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

http://link.springer.com/article/10.1007/s12559-018-9598-1/fulltext.html

Reference38 articles.

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2. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L. Imagenet: a large-scale hierarchical image database. IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009 IEEE, pp. 248–255; 2009.

3. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 2012;25(2):2012.

4. Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3D shape recognition. IEEE International Conference on Computer Vision. IEEE Computer Society; 2015. p. 945–953.

5. Rifai S, Vincent P, Muller X, Glorot X, Bengio Y. Contractive auto-encoders: explicit invariance during feature extraction. Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 833–840; 2011.

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