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Classifying images using restricted Boltzmann machines and convolutional neural networks
Author(s): Zhijun Zhao; Tongde Xu; Chenyu Dai
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Paper Abstract

To improve the feature recognition ability of deep model transfer learning, we propose a hybrid deep transfer learning method for image classification based on restricted Boltzmann machines (RBM) and convolutional neural networks (CNNs). It integrates learning abilities of two models, which conducts subject classification by exacting structural higher-order statistics features of images. While the method transfers the trained convolutional neural networks to the target datasets, fully-connected layers can be replaced by restricted Boltzmann machine layers; then the restricted Boltzmann machine layers and Softmax classifier are retrained, and BP neural network can be used to fine-tuned the hybrid model. The restricted Boltzmann machine layers has not only fully integrated the whole feature maps, but also learns the statistical features of target datasets in the view of the biggest logarithmic likelihood, thus removing the effects caused by the content differences between datasets. The experimental results show that the proposed method has improved the accuracy of image classification, outperforming other methods on Pascal VOC2007 and Caltech101 datasets.

Paper Details

Date Published: 21 July 2017
PDF: 9 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104202U (21 July 2017); doi: 10.1117/12.2281994
Show Author Affiliations
Zhijun Zhao, Guangzhou Univ. (China)
Tongde Xu, Guangdong AIB Polytechnic College (China)
Chenyu Dai, China Mobile Group Guangdong Co., Ltd. (China)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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