Application of Improved Convolutional Neural Network in Image Classification

作者：Enzhi Chen, Xiaming Wu, Chunyang Wang, Yanyan Du · 年份：2019 · DOI：10.1109/mlbdbi48998.2019.00027 · 被引用次数：12 · 研究领域：Image Retrieval and Classification Techniques、Face recognition and analysis、Advanced Image and Video Retrieval Techniques

Because the LeNet-5 convolutional neural network is not accurate in classifying images with complex texture features, in order to improve the accuracy, this paper proposes a multi-convolution neural network. First, on the basis of the LeNet-5 network, a convolutional layer and a pooling layer are added to deepen the depth of the network. Secondly, the first two pooling layers are propagated backward and connected to the last pooling layer through convolution, so as to make full use of the low-level features extracted by the network and effectively combining the low-level features and high-level features extracted by the network. Finally, use the Adam optimizer to adjust and update the parameters of the network. Experimental results on the Cats vs. Dogs, Cifar-10 and Fer2013 datasets show that the multi-convolution neural network has good classification ability on complex texture feature datasets.