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

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Abstract

Fast and accurate detection of 3D shapes is a fundamental task of robotic systems for intelligent tracking and automatic control. View-based 3D shape recognition has attracted increasing attention because human perceptions of 3D objects mainly rely on multiple 2D observations from different viewpoints. However, most existing multi-view-based cognitive computation methods use straightforward pairwise comparisons among the projected images then follow with weak aggregation mechanism, which results in heavy computation cost and low recognition accuracy. To address such problems, a novel network structure combining multi-view convolutional neural networks (M-CNNs), extreme learning machine auto-encoder (ELM-AE), and ELM classifer, named as MCEA, is proposed for comprehensive feature learning, effective feature aggregation, and efficient classification of 3D shapes. Such novel framework exploits the advantages of deep CNN architecture with the robust ELM-AE feature representation, as well as the fast ELM classifier for 3D model recognition. Compared with the existing set-to-set image comparison methods, the proposed shape-to-shape matching strategy could convert each high informative 3D model into a single compact feature descriptor via cognitive computation. Moreover, the proposed method runs much faster and obtains a good balance between classification accuracy and computational efficiency. Experimental results on the benchmarking Princeton ModelNet, ShapeNet Core 55, and PSB datasets show that the proposed framework achieves higher classification and retrieval accuracy in much shorter time than the state-of-the-art methods.

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References

  1. Wang F, Kang L, Li Y. 2015. Sketch-based 3D shape retrieval using convolutional neural networks, Computer Science.

  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.

    Google Scholar 

  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.

  6. Achlioptas D. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of computer and System Sciences 2003;66(4):671–687.

    Article  Google Scholar 

  7. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature 1999;401 (6755):788–791.

    Article  CAS  PubMed  Google Scholar 

  8. Kasun LLC, Yang Y, Huang G-B, Zhang Z. Dimension reduction with extreme learning machine. IEEE Trans Image Process 2016;25(8):3906–3918.

    Article  PubMed  Google Scholar 

  9. Qi CR, Su H, Mo K, Guibas LJ. Pointnet: deep learning on point sets for 3D classification and segmentation. Proc. Computer Vision and Pattern Recognition (CVPR), IEEE 2017;1(2):4.

    Google Scholar 

  10. Li Y, Bu R, Sun M, Chen B. 2018. Pointcnn. arXiv:1801.07791.

  11. Wu J, Zhang C, Xue T, Freeman B, Tenenbaum J. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. Advances in Neural Information Processing Systems, pp. 82–90; 2016.

  12. Tatarchenko M, Dosovitskiy A, Brox T. 2017. Octree generating networks: efficient convolutional architectures for high-resolution 3d outputs. arXiv:1703.09438.

  13. Yi L, Kim VG, Ceylan D, Shen I, Yan M, Su H, Lu C, Huang Q, Sheffer A, Guibas L, et al. A scalable active framework for region annotation in 3D shape collections. ACM Trans Graph (TOG) 2016;35(6):210.

    Article  Google Scholar 

  14. Maturana D, Scherer S. Voxnet: a 3D convolutional neural network for real-time object recognition. International Conference on Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ. IEEE, pp. 922–928; 2015.

  15. Wu Z, Song S, Khosla A, Yu F, Zhang L, Tang X, Xiao J. 2015. 3D shapenets: a deep representation for volumetric shapes, Eprint Arxiv, pp. 1912–1920.

  16. Nchez J, Perronnin F, Mensink T, Verbeek J. Image classification with the fisher vector: theory and practice. Int J Comput Vis 2013;105(3):222–245.

    Article  Google Scholar 

  17. Eitz M, Richter R, Boubekeur T, Hildebrand K, Alexa M. Sketch-based shape retrieval. ACM Trans Graph 2012;31(4):31–1.

    Google Scholar 

  18. Chen D-Y, Tian X-P, Shen Y-T, Ouhyoung M. On visual similarity based 3d model retrieval. Computer Graphics Forum, vol. 122, no. 3. Wiley Online Library, pp. 223–232; 2003.

  19. Shih J-L, Lee C-H, Wang JT. A new 3D model retrieval approach based on the elevation descriptor. Pattern Recogn 2007;40(1):283–295.

    Article  Google Scholar 

  20. Kazhdan M, Funkhouser T, Rusinkiewicz S. Rotation invariant spherical harmonic representation of 3D shape descriptors. Eurographics/acm SIGGRAPH Symposium on Geometry Processing, pp. 156–164; 2003.

  21. Bai S, Bai X, Zhou Z, Zhang Z, Latecki LJ. Gift: a real-time and scalable 3D shape search engine. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. IEEE, pp. 5023–5032; 2016.

  22. Huang GB, Zhou H, Ding X, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern 2012;42(2):513–529.

    Article  Google Scholar 

  23. Huang GB, Cambria E, Toh KA, Widrow B, Xu Z. New trends of learning in computational intelligence. IEEE Comput Intell Mag 2015;10(2):16–17.

    Article  Google Scholar 

  24. Zhang PB, Yang ZX. A novel adaboost framework with robust threshold and structural optimization. IEEE Transactions on Cybernetics 2018;48(1):64–76.

    Article  PubMed  Google Scholar 

  25. Huang G, Huang GB, Song S, You K. Trends in extreme learning machines: a review. Neural Netw 2015;61:32–48.

    Article  PubMed  Google Scholar 

  26. Yang ZX, Wang XB, Wong PK. Single and simultaneous fault diagnosis with application to a multistage gearbox: a versatile dual-elm network approach. IEEE Trans Ind Inf 2018;PP(99):1–1.

    Google Scholar 

  27. Zhou H, Huang GB, Lin Z, Wang H, Soh YC. Stacked extreme learning machines. IEEE Transactions on Cybernetics 2015;45(9):2013–2025.

    Article  PubMed  Google Scholar 

  28. Phong BT. Illumination for computer generated pictures. Commun ACM 1975;18(6):311–317.

    Article  Google Scholar 

  29. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–444.

    Article  CAS  PubMed  Google Scholar 

  30. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M. Imagenet large scale visual recognition challenge. Int J Comput Vis 2015;115(3):211–252.

    Article  Google Scholar 

  31. Tang J, Deng C, Huang GB. Extreme learning machine for multilayer perceptron. IEEE Transactions on Neural Networks and Learning Systems 2016;27(4):809–821.

    Article  PubMed  Google Scholar 

  32. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 2010;11(Dec): 3371–3408.

    Google Scholar 

  33. Shilane P, Min P, Kazhdan M, Funkhouser T. The princeton shape benchmark. Shape modeling applications, 2004. Proceedings. IEEE, pp. 167–178; 2004.

  34. Savva M, Yu F, Su H, Kanezaki A, Furuya T, Ohbuchi R, Zhou Z, Yu R, Bai S, Bai X, Aono M, Tatsuma A, Thermos S, Axenopoulos A, Papadopoulos GT, Daras P, Deng X, Lian Z, Li B, Johan H, Lu Y, Mk S. Large-scale 3D shape retrieval from shapenet Core55. Eurographics Workshop on 3D Object Retrieval. In: Pratikakis I, Dupont F, and Ovsjanikov M, editors; 2017. The Eurographics Association.

  35. Trimble. 3D warehouse. 2012. https://3dwarehouse.sketchup.com/.

  36. Shi B, Bai S, Zhou Z, Bai X. Deeppano: Deep panoramic representation for 3-D shape recognition. IEEE Signal Process Lett 2015;22(12):2339–2343.

    Article  Google Scholar 

  37. Xie Z, Xu K, Shan W, Liu L, Xiong Y, Huang H. Projective feature learning for 3D shapes with multi-view depth images. Comput Graphics Forum 2015;7:34.

    Google Scholar 

  38. Maaten Lvd, Hinton G. Visualizing data using t-sne. J Mach Learn Res 2008;9(Nov):2579–2605.

    Google Scholar 

Download references

Funding

This study was supported in part by the Science and Technology Development Fund of Macao S.A.R (FDCT) under grant FDCT/121/2016/A3 and MoST-FDCT Joint Grant 015/2015/AMJ, in part by University of Macau under grant MYRG2016-00160-FST.

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Authors and Affiliations

  1. Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau SAR, China

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

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  1. Zhi-Xin Yang
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  2. Lulu Tang
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  3. Kun Zhang
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  4. Pak Kin Wong
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Correspondence to Lulu Tang.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Cite this article

Yang, ZX., Tang, L., Zhang, K. et al. Multi-View CNN Feature Aggregation with ELM Auto-Encoder for 3D Shape Recognition. Cogn Comput 10, 908–921 (2018). https://doi.org/10.1007/s12559-018-9598-1

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  • DOI: https://doi.org/10.1007/s12559-018-9598-1

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