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
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.
刘琼, 李宗贤, 孙富春, 田永鸿, 曾炜. 基于深度信念卷积神经网络的图像识别与分类[J]. 清华大学学报（自然科学版）, 2018, 58(9): 781-787.
LIU Qiong, LI Zongxian, SUN Fuchun, TIAN Yonghong, ZENG Wei. Image recognition and classification by deep belief-convolutional neural networks. Journal of Tsinghua University(Science and Technology), 2018, 58(9): 781-787.
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