关键词: 图像分类, 自编码器, 卷积神经网络, 双并行交叉

Abstract: In order to deal with the classification problem of a large number of disturbed images, a double parallel crossover noise reduction convolution model is proposed. The model consists of two parts of parallel cross-network structure, corresponding to improved self-coding and parallel cross-convolutional neural network. At the same time, the model uses the method of batch normalization and improvement of traditional activation function in the model training. It is proved by experiments that the model has the characteristics of strong noise reduction, good robustness, strong generalization ability and high accuracy in comparison with the same model. Secondly, it avoids overfitting and speeds up convergence. In the case of different degrees of picture damage, it can also accomplish the task of image target recognition classification well.

Key words: image classification, autoencoder, convolution neural networks, double parallel cross