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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-46672-9_66
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