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Image recognition and classification by deep belief-convolutional neural networks |
| LIU Qiong1, LI Zongxian2, SUN Fuchun3, TIAN Yonghong2, ZENG Wei2 |
1. School of Automation, Beijing Information Science and Technology University, Beijing 100192, China;
2. National Engineering Laboratory for Video Technology, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
3. State Key Laboratory of Intelligence Technology and System, Department of Computer Science, Tsinghua University, Beijing 100084, China |
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Abstract Convolutional neural network (CNN) would easily converge to the local minimum if the network was randomly initialized in image classification tasks. A deep belief network pre-training method was developed by merging unsupervised and supervised methods. Feature sets were extracted from the image patches of zero component analysis (ZCA) whitening and deep belief pre-training to initialize weights of CNNs. Then, convolution features were extracted from the training samples by applying convolution and pooling operations and classified to a specific category through a fully connected network. Finally, the loss value was computed for global optimization. Extensive experimental evaluations on some public datasets show that this method is simple but very effective with the error rate decrease of 0.1% on MNIST and the accuracy increase of 0.56% on Caltech101, which indicates that this method is superior to similar methods.
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| Keywords
deep belief networks
image recognition
convolutional neural networks
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Issue Date: 19 September 2018
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2018,
Vol. 58
