A novel approach to cloth classification through deep neural networks

Abstract

The recent development of the field of artificial intelligence makes the traditional technical recognition more accurate. An important area is commodity identification which helps to classify commodity and provide information for data-ming and commercial decision. This paper considers cloth classification by means of deep neural networks. We summarize the existing methods: the effect improvement of network can be divided into two kinds, by modifying network structure according to their priorities, i.e., increase the depth of network and enhance the performance of convolution unit. In order to further improve the performance of network model, we redesign the network structure based on AlexNet, and put forward the deep convolution neural network model. Experiments are performed on the data sets including ImageNet-1000 and cloth data sets ACS and CAPB. The results show that the proposed deep convolutional neural network is superior to the original AlexNet on these three data sets in terms of accuracy.