Learning to align from scratch

Advances in Neural Information Processing Systems

Volumn 1, Issue , 2012, Pages 764-772

  Huang, Gary B ^a   Mattar, Marwan A ^a   Lee, Honglak ^b   Learned Miller, Erik ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FACE VERIFICATION; FEATURE REPRESENTATION; GROUP SPARSITIES; IMAGE DESCRIPTORS; IMPROVE PERFORMANCE; RESTRICTED BOLTZMANN MACHINE; UNSUPERVISED ALGORITHMS; UNSUPERVISED FEATURE LEARNING;

COMPLEX NETWORKS; FACE RECOGNITION; IMAGE MATCHING; LEARNING ALGORITHMS;

ALIGNMENT;

EID: 84877745091     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (41)

1. L. Wolf, T. Hassner, and Y. Taigman. Similarity scores based on background samples. In ACCV, 2009.
2. Y. Taigman, L. Wolf, and T. Hassner. Multiple one-shots for utilizing class label information. In BMVC, 2009.
3. M. Everingham, J. Sivic, and A. Zisserman. "Hello! My name is⋯ Buffy" - automatic naming of characters in TV video. In BMVC, 2006.
4. T. L. Berg, A. C. Berg, M. Maire, R. White, Y. W. Teh, E. Learned-Miller, and D. A. Forsyth. Names and faces in the news. In CVPR, 2004.
5. Y. Zhou, L. Gu, and H.-J. Zhang. Bayesian tangent shape model: Estimating shape and pose parameters via Bayesian inference. In CVPR, 2003.
6. E. Learned-Miller. Data driven image models through continuous joint alignment. PAMI, 2005.
7. E. Miller, N. Matsakis, and P. Viola. Learning from one example through shared densities on transforms. In CVPR, 2000.
8. L. Zollei, E. Learned-Miller, E. Grimson, and W. Wells. Efficient population registration of 3d data. In Workshop on Computer Vision for Biomedical Image Applications: Current Techniques and Future Trends, at ICCV, 2005.
9. E. Learned-Miller and V. Jain. Many heads are better than one: Jointly removing bias from multiple MRIs using nonparametric maximum likelihood. In Proceedings of Information Processing in Medical Imaging, pages 615-626, 2005.
10. G. B. Huang, V. Jain, and E. Learned-Miller. Unsupervised joint alignment of complex images. In ICCV, 2007.
11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. IJCV, 60(2):91-110, 2004.
12. G. E. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
13. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. Greedy layer-wise training of deep networks. In NIPS, 2007.
14. M. Ranzato, Y.-L. Boureau, and Y. LeCun. Sparse feature learning for deep belief networks. In NIPS, 2007.
15. M. Cox, S. Lucey, S. Sridharan, and J. Cohn. Least squares congealing for unsupervised alignment of images. In CVPR, 2008.
16. M. Cox, S. Sridharan, S. Lucey, and J. Cohn. Least squares congealing for large numbers of images. In ICCV, 2009.
17. X. Liu, Y. Tong, and F. W. Wheeler. Simultaneous alignment and clustering for an image ensemble. In ICCV, 2009.
18. M. A. Mattar, A. R. Hanson, and E. G. Learned-Miller. Unsupervised joint alignment and clustering using Bayesian nonparametrics. In UAI, 2012.
19. J. Zhu, L. V. Gool, and S. C. Hoi. Unsupervised face alignment by nonrigid mapping. In ICCV, 2009.
20. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381:607-609, 1996.
21. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS, 2007.
22. M. Zeiler, D. Krishnan, G. Taylor, and R. Fergus. Deconvolutional networks. In CVPR, 2010.
23. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM, 54(10):95-103, 2011.
24. J. Yang, K. Yu, Y. Gong, and T. S. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, pages 1794-1801, 2009.
25. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.
26. G. B. Huang, H. Lee, and E. Learned-Miller. Learning hierarchical representations for face verification with convolutional deep belief networks. In CVPR, 2012.
27. H. Lee, Y. Largman, P. Pham, and A. Y. Ng. Unsupervised feature learning for audio classification using convolutional deep belief networks. In NIPS, 2009.
28. R. Collobert and J. Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML, 2008.
29. R. Salakhutdinov and G. E. Hinton. Semantic hashing. International Journal of Approximate Reasoning, 50:969-978, 2009.
30. R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught learning: Transfer learning from unlabeled data. In ICML, 2007.
31. O. Chapelle, B. Schölkopf, and A. Zien. Semi-supervised learning. MIT Press, 2006.
32. M. Yuan and L. Yin. Model selection and estimation in regression with grouped variables. Technical report, University of Wisconsin, 2004.
33. A. Hyvärinen, P. O. Hoyer, and M. Inki. Topographic independent component analysis. Neural Computation, 13(7):1527-1558, 2001.
34. K. Kavukcuoglu, M. Ranzato, R. Fergus, and Y. LeCun. Learning invariant features through topographic filter maps. In CVPR, 2009.
35. M. Norouzi, M. Ranjbar, and G. Mori. Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning. In CVPR, pages 2735-2742, 2009.
36. Y.-L. Boureau, F. R. Bach, Y. LeCun, and J. Ponce. Learning mid-level features for recognition. In CVPR, 2010.
37. G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771-1800, 2002.
38. H. Lee, C. Ekanadham, and A. Y. Ng. Sparse deep belief net model for visual area V2. In NIPS, 2008.
39. K. Sohn, D. Y. Jung, H. Lee, and A. H. III. Efficient learning of sparse, distributed, convolutional feature representations for object recognition. In ICCV, 2011.
40. H. V. Nguyen and L. Bai. Cosine similarity metric learning for face verification. In ACCV, 2010.
41. T. Ojala, M. Pietikinen, and D. Harwood. A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 19(3):51-59, 1996.