Review of Image Classification Algorithms Based on Convolutional Neural Networks

With the arrival of big data and the improvement of computing power, deep learning (DL) has swept the world. The traditional image classification method is difficult to process huge image data and cannot meet the requirements of image classification accuracy and speed. The image classification method based on convolutional neural network (CNN) breaks through the bottleneck of traditional image classification methods. It has become the mainstream image classification algorithm. Therefore, how to effectively use the convolutional neural network to classify images has become a hot topic in the field of computer vision at home and abroad. In this paper, after systematic research on convolutional neural networks and indepth study of the application of convolutional neural networks in image processing, the mainstream structural models and their advantages and disadvantages, time/space complexity are presented. Problems that may be encountered in the process of model training used in image classification based on convolutional neural networks and corresponding solutions are also given. At the same time, it also introduces the generative adversarial network and capsule network of image classification extension model based on deep learning. Then, the simulation results show that the image classification method based on convolutional neural network is superior to the traditional image classification method in image classification accuracy. At the same time, the performance differences between the current popular convolutional neural network models are compared and the advantages and disadvantages are further verified. Finally, Experiments and explanations on over-fitting problems, dataset construction methods, generative adversarial network and capsule network performance are given.