Deep learning representation using autoencoder for 3D shape retrieval

Proceedings 2014 IEEE International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2014

Volumn , Issue , 2014, Pages 279-284

  Zhu, Zhuotun ^a   Wang, Xinggang ^a   Bai, Song ^a   Yao, Cong ^a   Bai, Xiang ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARKING; LEARNING SYSTEMS; OBJECT DETECTION; OBJECT RECOGNITION;

3D SHAPE RECOGNITION; 3D SHAPE RETRIEVAL; COMPLEX STRUCTURE; FEATURE LEARNING; LOCAL DESCRIPTORS; LOCAL IMAGE DESCRIPTORS; STATE-OF-THE-ART PERFORMANCE; VISUAL APPLICATIONS;

DEEP LEARNING;

EID: 84920743358     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SPAC.2014.6982699     Document Type: Conference Paper

References (26)

1. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1 (1989) 541-551
2. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS. Volume 1. (2012) 4
3. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. arXiv preprint arXiv:1311.2524 (2013)
4. Krizhevsky, A., Hinton, G.E.: Using very deep autoencoders for content-based image retrieval. In: ESANN, Citeseer (2011)
5. Zhang, J., Shan, S., Kan, M., Chen, X.: Coarse-to-fine auto-encoder networks (cfan) for real-time face alignment. In: European Conference on Computer Vision. (2014)
6. Chen, D., Tian, X., Shen, Y., Ouhyoung, M.: On visual similarity based 3D model retrieval. Computer Graphics Forum 22 (2003) 223-232
7. Lian, Z., Godil, A., Sun, X.: Visual similarity based 3d shape retrieval using bag-of-features. In: Shape Modeling International Conference (SMI), 2010. (2010) 25-36
8. Shilane, P., Min, P., Kazhdan, M., Funkhouser, T.: The princeton shape benchmark. In: Shape Modeling Applications, 2004. Proceedings, IEEE (2004) 167-178
9. Cyr, C.M., Kimia, B.B.: 3D object recognition using shape similaritybased aspect graph. In: International Conference on Computer Vision. (2001) 254-261
10. Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3D model retrieval. In: Shape Modeling International. (2008) 93-102
11. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60 (2004) 91-110
12. Li, F., Pietro, P.: A bayesian hierarchical model for learning natural scene categories. In: Computer Vision and Pattern Recognition, 2005. IEEE Computer Society Conference on. Volume 2., IEEE (2005) 524-531
13. Vranic, D.V.: DESIRE: a composite 3D-shape descriptor. In: International Conference on Multimedia Computing and Systems/International Conference on Multimedia and Expo. (2005) 962-965
14. Papadakis, P., Pratikakis, I., Theoharis, T., Passalis, G., Perantonis, S.J., Paraskevi, A.: 3D object retrieval using an efficient and compact hybrid shape descriptor. (2008) 9-16
15. Papadakis, P., Pratikakis, I., Theoharis, T., Perantonis, S.J.: PANORAMA: A 3D shape descriptor based on panoramic views for unsupervised 3D object retrieval. International Journal of Computer Vision 89 (2010) 177-192
16. Lian, Z., Rosin, P.L., Sun, X.: Rectilinearity of 3D meshes. International Journal of Computer Vision 89 (2010) 130-151
17. Bai, X., Rao, C., Wang, X.: Shape vocabulary: A robust and efficient shape representation for shape matching. IEEE Transactions on Image Processing 29 (2014) 3935-3949
18. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. In: NIPS. (2006)
19. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18 (2006) 1527-1554
20. LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Computation 1 (1989) 541-551
21. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences 79 (1982) 2554-2558
22. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Computation 14 (2002) 1771-1800
23. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Computer Vision and Pattern Recognition. Volume 2. (2006) 2169-2178
24. Jayanti, S., Kalyanaraman, Y., Iyer, N., Ramani, K.: Developing an engineering shape benchmark for CAD models. Computer-aided Design 38 (2006) 939-953
25. Zhang, Z., Liang, X., Ganesh, A., Ma, Y.: TILT: transform invariant low-rank textures. (In: Asian Conference on Computer Vision 2010)
26. Bai, X., Yang, X., Latecki, L., Liu, W., Tu, Z.: Learning contextsensitive shape similarity by graph transduction. IEEE Transactions on Pattern Anal. Mach. Intell. 32 (2010) 861-874