publication . Conference object . Preprint . 2016

Joint visual denoising and classification using deep learning

Chen, Gang; Li, Yawei; Srihari, Sargur N.;
Open Access
  • Published: 04 Dec 2016
  • Publisher: IEEE
Abstract
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using s...
Subjects
free text keywords: Artificial intelligence, business.industry, business, Image restoration, Noise measurement, Deep learning, MNIST database, Categorization, Computer vision, Machine learning, computer.software_genre, computer, Visualization, Pattern recognition, Autoencoder, Backpropagation, Computer science, Computer Science - Computer Vision and Pattern Recognition
19 references, page 1 of 2

[5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Comput., vol. 1, no. 4, pp. 541-551, Dec. 1989.

[6] H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio, “Learning algorithms for the classification restricted boltzmann machine,” J. Mach. Learn. Res., vol. 13, no. 1, pp. 643-669, Mar. 2012. [Online]. Available: http://dl.acm.org/citation.cfm?id=2503308.2188407

[7] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.

[8] J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning.” in ICML, 2011, pp. 689-696. [OpenAIRE]

[9] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527-1554, Jul. 2006. [OpenAIRE]

[10] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” TPAMI, 2012.

[11] N. Srivastava and R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” Journal of Machine Learning Research, vol. 15, pp. 2949-2980, 2014.

[12] L. I. Rudin and S. Osher, “Total variation based image restoration with free local constraints.” in ICIP. IEEE, 1994, pp. 31-35.

[13] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in ICCV. Washington, DC, USA: IEEE Computer Society, 1998, pp. 839-.

[14] J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of gaussians in the wavelet domain,” IEEE Trans. Image Process, vol. 12, pp. 1338-1351, 2003.

[15] V. Jain and H. S. Seung, “Natural image denoising with convolutional networks.” in NIPS. Curran Associates, Inc., 2008, pp. 769-776.

[16] G. Chen, C. Xiong, and J. J. Corso, “Dictionary transfer for image denoising via domain adaptation,” in 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, Orlando, FL, USA, September 30 - October 3, 2012, 2012, pp. 1189-1192.

[17] G. Chen and S. N. Srihari, “Removing structural noise in handwriting images using deep learning,” in ICVGIP, 2014.

[18] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” JMLR, 2010.

[19] H. Larochelle and Y. Bengio, “Classification using discriminative restricted boltzmann machines,” in Proceedings of the 25th International Conference on Machine Learning, ser. ICML '08. New York, NY, USA: ACM, 2008, pp. 536-543.

19 references, page 1 of 2
Abstract
Visual restoration and recognition are traditionally addressed in pipeline fashion, i.e. denoising followed by classification. Instead, observing correlations between the two tasks, for example clearer image will lead to better categorization and vice visa, we propose a joint framework for visual restoration and recognition for handwritten images, inspired by advances in deep autoencoder and multi-modality learning. Our model is a 3-pathway deep architecture with a hidden-layer representation which is shared by multi-inputs and outputs, and each branch can be composed of a multi-layer deep model. Thus, visual restoration and classification can be unified using s...
Subjects
free text keywords: Artificial intelligence, business.industry, business, Image restoration, Noise measurement, Deep learning, MNIST database, Categorization, Computer vision, Machine learning, computer.software_genre, computer, Visualization, Pattern recognition, Autoencoder, Backpropagation, Computer science, Computer Science - Computer Vision and Pattern Recognition
19 references, page 1 of 2

[5] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Comput., vol. 1, no. 4, pp. 541-551, Dec. 1989.

[6] H. Larochelle, M. Mandel, R. Pascanu, and Y. Bengio, “Learning algorithms for the classification restricted boltzmann machine,” J. Mach. Learn. Res., vol. 13, no. 1, pp. 643-669, Mar. 2012. [Online]. Available: http://dl.acm.org/citation.cfm?id=2503308.2188407

[7] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504-507, Jul. 2006.

[8] J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng, “Multimodal deep learning.” in ICML, 2011, pp. 689-696. [OpenAIRE]

[9] G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural Comput., vol. 18, no. 7, pp. 1527-1554, Jul. 2006. [OpenAIRE]

[10] Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” TPAMI, 2012.

[11] N. Srivastava and R. Salakhutdinov, “Multimodal learning with deep boltzmann machines,” Journal of Machine Learning Research, vol. 15, pp. 2949-2980, 2014.

[12] L. I. Rudin and S. Osher, “Total variation based image restoration with free local constraints.” in ICIP. IEEE, 1994, pp. 31-35.

[13] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in ICCV. Washington, DC, USA: IEEE Computer Society, 1998, pp. 839-.

[14] J. Portilla, V. Strela, M. J. Wainwright, and E. P. Simoncelli, “Image denoising using scale mixtures of gaussians in the wavelet domain,” IEEE Trans. Image Process, vol. 12, pp. 1338-1351, 2003.

[15] V. Jain and H. S. Seung, “Natural image denoising with convolutional networks.” in NIPS. Curran Associates, Inc., 2008, pp. 769-776.

[16] G. Chen, C. Xiong, and J. J. Corso, “Dictionary transfer for image denoising via domain adaptation,” in 19th IEEE International Conference on Image Processing, ICIP 2012, Lake Buena Vista, Orlando, FL, USA, September 30 - October 3, 2012, 2012, pp. 1189-1192.

[17] G. Chen and S. N. Srihari, “Removing structural noise in handwriting images using deep learning,” in ICVGIP, 2014.

[18] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” JMLR, 2010.

[19] H. Larochelle and Y. Bengio, “Classification using discriminative restricted boltzmann machines,” in Proceedings of the 25th International Conference on Machine Learning, ser. ICML '08. New York, NY, USA: ACM, 2008, pp. 536-543.

19 references, page 1 of 2
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publication . Conference object . Preprint . 2016

Joint visual denoising and classification using deep learning

Chen, Gang; Li, Yawei; Srihari, Sargur N.;