Abstract
Image feature extraction is an essential step in image recognition. In this paper, taking the benefits of the effectiveness of Gaussian-Bernoulli Restricted Boltzmann Machine (GRBM) for learning discriminative image features and the capability of Convolutional Neural Network (CNN) for learning spatial features, we propose a hybrid model called Convolutional Gaussian-Bernoulli Restricted Boltzmann Machine (CGRBM) for image feature extraction by combining GRBM with CNN. Experimental results implemented on some benchmark datasets showed that our model is more effective for natural images recognition tasks than some popular methods, which is suggested that our proposed method is a potential applicable method for real-valued image feature extraction and recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE International Computer Vision and Pattern Recognition (CVPR), pp. 886–893. IEEE (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)
Rumelhart, D.E., McClelland, J.L., PDP Research Group: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1–2 (1986)
Hinton, G., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798. ACM (2007)
Wang, N., Melchior, J., Wiskott, L.: Gaussian-binary restricted Boltzmann machines on modeling natural image statistics. arXiv preprint (2014) arXiv:1401.5900
Sutskever, I., Hinton, G.E., Taylor, G.W.: The recurrent temporal restricted Boltzmann machine. In: Advances in Neural Information Processing Systems, pp. 1601–1608 (2008)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR (2014)
Norouzi, M., Ranjbar, M., Mori, G.: Stacks of convolutional restricted Boltzmann machines for shift-invariant feature learning. In: CVPR (2009)
Hinton, G.: A practical guide to training restricted Boltzmann machines. Momentum 9(1), 926 (2010)
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: ICML (2009)
Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Montral, U.D., Qubec, M.: Greedy layer-wise training of deep networks. In: NIPS. MIT Press (2007)
Tieleman, T.: Training restricted Boltzmann machines using approximations to the likelihood gradient. In: Proceedings of the 25th International Conference on Machine Learning. ICML 2008, pp. 1064–1071. ACM Press, New York (2008)
Cho, K.H., Raiko, T., Ilin, A.: Enhanced gradient for training restricted Boltzmann machines. Neural Comput. 25(3), 805–831 (2013)
Acknowledgments
This work was supported by National Natural Science Foundation of China under grant no. 51473088 and National Key Research and Development Plan of China under grant no. 2016YFC0301400.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Li, Z., Cai, X., Liang, T. (2016). Gaussian-Bernoulli Based Convolutional Restricted Boltzmann Machine for Images Feature Extraction. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-46672-9_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-46671-2
Online ISBN: 978-3-319-46672-9
eBook Packages: Computer ScienceComputer Science (R0)