Development of convolutional neural network and its application in image classification: a survey

Abstract

In recent years, convolutional neural networks (CNNs) have been widely used in various computer visual recognition tasks and have achieved good results compared with traditional methods. Image classification is one of the basic and important tasks of visual recognition, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. We first summarize the development history of CNNs and then analyze the architecture of various deep CNNs in image classification. Furthermore, not only the innovation of the network architecture is beneficial to the results of image classification, but also the improvement of the network optimization method or training method has improved the result of image classification. We also analyze each of these methods' effect. The experimental results of various methods are compared. Finally, the development of future CNNs is prospected.

Image Target Recognition via Mixed Feature-Based Joint Sparse Representation

“…For extracting sparse features, we draw on the viewpoint of the literature [9][10][11] about network design. Visual geometry group networks (VGGNets) proposed by Simonyan and Zisserman have significantly improved image recognition performance by deepening the network to 19 layers.…”

Unify Language and Vision: An Efficient COVID-19 Tomography Image Classification Approach

“…Chen et al [6] trained image representation by sequence Transformers and tested on CIFAR-10 to show it is outperforming to Wide-ResNet which was inspired by unsupervised natural language representation learning. Wang et al [7] reviewed that convolutional neural networks had been proposed in the 1960s, and had its implementation in the 1980s, and until LeCun et al [8]'s first experiment on handwritten digit recognition, CNN's great potential had been revealed. In the 2010s, Krizhevsky et al [9] proposed the deep architecture, AlexNet, by concatenating multiple components of CNN layers.…”

A Novel Image Classification Approach via Dense-MobileNet Models

“…Deep convolutional neural network (CNN) has achieved significant success in the field of computer vision, such as image classification [1], target tracking [2], target detection [3], and semantic image segmentation [4,5]. For example, in the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012), Krizhevsky et al won the championship with an AlexNet [1] model of about 60 million parameters and eight layers.…”