Multimodal Deep Autoencoder for Human Pose Recovery

IEEE Transactions on Image Processing

Volumn 24, Issue 12, 2015, Pages 5659-5670

  Hong, Chaoqun ^a   Yu, Jun ^b   Wan, Jian ^b   Tao, Dacheng ^c   Wang, Meng ^d  

^a XIAMEN UNIVERSITY OF TECHNOLOGY   (China)

^b HANGZHOU DIANZI UNIVERSITY   (China)

^c UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

^d HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

back propagation; deep learning; Human pose recovery; Hypergraph; multi modal learning

Indexed keywords

FEATURE EXTRACTION; IMAGE PROCESSING; LAPLACE TRANSFORMS; MAPPING; MATRIX ALGEBRA; NETWORK LAYERS; RECOVERY;

DEEP LEARNING; DEEP NEURAL NETWORKS; FEATURE DESCRIPTION; HUMAN POSE; HYPERGRAPH; LOW-RANK REPRESENTATIONS; MULTI-MODAL LEARNING; RECOVERY PERFORMANCE;

BACKPROPAGATION;

ARTIFICIAL INTELLIGENCE; BODY POSTURE; FEMALE; HUMAN; IMAGE PROCESSING; MALE; PHYSIOLOGY; PROCEDURES; VIDEORECORDING;

ARTIFICIAL INTELLIGENCE; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MALE; POSTURE; VIDEO RECORDING;

EID: 84953943377     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2015.2487860     Document Type: Article

References (43)

1. H. Zhou, T. Zhang, W. Lu, "Vision-based pose estimation from points with unknown correspondences," IEEE Trans. Image Process., vol. 23, no. 8, pp. 3468-3477, Aug. 2014.
2. J. Shen et al., "Unified structured learning for simultaneous human pose estimation and garment attribute classification," IEEE Trans. Image Process., vol. 23, no. 11, pp. 4786-4798, Nov. 2014.
3. I. Everts, J. C. van Gemert, T. Gevers, "Evaluation of color spatiotemporal interest points for human action recognition," IEEE Trans. Image Process., vol. 23, no. 4, pp. 1569-1580, Apr. 2014.
4. H. Sidenbladh, M. J. Black, D. J. Fleet, "Stochastic tracking of 3D human figures using 2D image motion," in Proc. 6th Eur. Conf. Comput. Vis., 2000, pp. 702-718.
5. M.W. Lee and R. Nevatia, "Human pose tracking in monocular sequence using multilevel structured models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 1, pp. 27-38, Jan. 2009.
6. A. Agarwal and B. Triggs, "Recovering 3D human pose from monocular images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 1, pp. 44-58, Jan. 2006.
7. G. Mori and J. Malik, "Estimating human body configurations using shape context matching," in Proc. 7th Eur. Conf. Comput. Vis., vol. 2. 2002, pp. 666-680.
8. M. Brand, "Shadow puppetry," in Proc. 7th IEEE Int. Conf. Comput. Vis., Sep. 1999, pp. 1237-1244.
9. R. Rosales and S. Sclaroff, "Inferring body pose without tracking body parts," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., vol. 2. Jun. 2000, pp. 721-727.
10. G. Shakhnarovich, P. Viola, T. Darrell, "Fast pose estimation with parameter-sensitive hashing," in Proc. 9th IEEE Int. Conf. Comput. Vis., vol. 2. Oct. 2003, pp. 750-757.
11. N. R. Howe, M. E. Leventon, W. T. Freeman, "Bayesian reconstruction of 3D human motion from single-camera video," in Proc. Adv. Neural Inf. Process. Syst., 1999, pp. 820-826.
12. W. Zhang, L. Shang, A. B. Chan, "A robust likelihood function for 3D human pose tracking," IEEE Trans. Image Process., vol. 23, no. 12, pp. 5374-5389, Dec. 2014.
13. K.-F. Yang, C.-Y. Li, Y.-J. Li, "Multifeature-based surround inhibition improves contour detection in natural images," IEEE Trans. Image Process., vol. 23, no. 12, pp. 5020-5032, Dec. 2014.
14. J. Liu, Y. Chen, J. Zhang, Z. Xu, "Enhancing low-rank subspace clustering by manifold regularization," IEEE Trans. Image Process., vol. 23, no. 9, pp. 4022-4030, Sep. 2014.
15. L. Bo and C. Sminchisescu, "Twin Gaussian processes for structured prediction," Int. J. Comput. Vis., vol. 87, nos. 1-2, pp. 28-52, 2010.
16. N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
17. Y. Bengio, "Learning deep architectures for AI," Found. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.
18. A. Toshev and C. Szegedy, "Deeppose: Human pose estimation via deep neural networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 1653-1660.
19. S. Li and A. B. Chan, "3D human pose estimation from monocular images with deep convolutional neural network," in Proc. 12th Asian Conf. Comput. Vis., 2014, pp. 332-347.
20. Y. Tian, L. Sigal, H. Badino, F. De la Torre, Y. Liu, "Latent Gaussian mixture regression for human pose estimation," in Proc. 10th Asian Conf. Comput. Vis., 2010, pp. 679-690.
21. Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle, "Greedy layerwise training of deep networks," in Proc. Adv. Neural Inf. Process. Syst., vol. 4. 2006, pp. 153-160.
22. P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, "Extracting and composing robust features with denoising autoencoders," in Proc. 25th Int. Conf. Mach. Learn., vol. 4. 2008, pp. 1096-1103.
23. M. Yang, G. Qiu, J. Huang, D. Elliman, "Near-duplicate image recognition and content-based image retrieval using adaptive hierarchical geometric centroids," in Proc. IEEE 18th Int. Conf. Pattern Recognit., Aug. 2006, pp. 958-961.
24. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Jun. 2005, pp. 886-893.
25. S. Belongie, J. Malik, J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 509-522, Apr. 2002.
26. G. Liu, Z. Lin, Y. Yu, "Robust subspace segmentation by low-rank representation," in Proc. 27th Int. Conf. Mach. Learn., vol. 3. 2010, pp. 663-670.
27. D. Tao, J. Cheng, M. Song, X. Lin, "Manifold ranking-based matrix factorization for saliency detection," IEEE Trans. Neural Netw. Learn. Syst., 2015, doi: 10.1109/TNNLS.2015.2461554.
28. D. Tao, X. Lin, L. Jin, X. Li, "Principal component 2D long shortterm memory for font recognition on single chinese characters," IEEE Trans. Cybern., 2015, doi: 10.1109/TCYB.2015.2414920.
29. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, A. Y. Ng, "Multimodal deep learning," in Proc. 28th Int. Conf. Mach. Learn., 2011, pp. 689-696.
30. N. Srivastava and R. R. Salakhutdinov, "Multimodal learning with deep Boltzmann machines," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp. 2222-2230.
31. D. Zhou, J. Huang, B. Schölkopf, "Learning with hypergraphs: Clustering, classification, embedding," in Proc. Adv. Neural Inf. Process. Syst., vol. 19. Sep. 2007, pp. 1601-1608.
32. B. Cheng, G. Liu, J. Wang, Z. Huang, S. Yan, "Multi-task lowrank affinity pursuit for image segmentation," in Proc. IEEE Int. Conf. Comput. Vis., Nov. 2011, pp. 2439-2446.
33. L. Sigal, A. O. Balan, M. J. Black, "HumanEva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion," Int. J. Comput. Vis., vol. 87, nos. 1-2, pp. 4-27, 2010.
34. C. Ionescu, D. Papava, V. Olaru, C. Sminchisescu, "Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments," IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, no. 7, pp. 1325-1339, Jul. 2014.
35. G. Mori, S. Belongie, J. Malik, "Efficient shape matching using shape contexts," IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 11, pp. 1832-1837, Nov. 2005.
36. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res., vol. 11, no. 11, pp. 3371-3408, 2010.
37. R. B. Palm, "Prediction as a candidate for learning deep hierarchical models of data," M.S. thesis, Dept. DTU Informat., Tech. Univ. Denmark, Lyngby, Denmark, 2012. [Online]. Available: http://www.imm.dtu.dk/English.aspx
38. C. Wang, Y. Wang, Z. Lin, A. L. Yuille, "Robust estimation of 3D human poses from a single image," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 2369-2376.
39. J. Mairal, F. Bach, J. Ponce, G. Sapiro, "Online dictionary learning for sparse coding," in Proc. 26th Annu. Int. Conf. Mach. Learn., 2009, pp. 689-696.
40. Y. Yang and D. Ramanan, "Articulated pose estimation with flexible mixtures-of-parts," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2011, pp. 1385-1392.
41. T. Xia, D. Tao, T. Mei, Y. Zhang, "Multiview spectral embedding," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 40, no. 6, pp. 1438-1446, Dec. 2010.
42. B. Sapp and B. Taskar, "MODEC: Multimodal decomposable models for human pose estimation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2013, pp. 3674-3681.
43. W. Ouyang, X. Chu, X. Wang, "Multi-source deep learning for human pose estimation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2014, pp. 2337-2344.