Street View Image Classification based on Convolutional Neural Network
This paper utilizes the deep learning algorithm to classify the Street View images.We did some research to find the appropriate convolutional neural network model that suits the classification of the street view images.We firstly collected our own dataset.Based on the convolutional neural network model AlexNet and according to the characteristics the dataset mentioned above to adjust the model structure and training methods.Experiments utilize max-pooling as sampling model,set the number of samples for one iteration as 128 and randomly cropped the same picture out as different parts for the input model to train reaches good accuracy rate which is 93.6%.The experiment result have shown that the number of samples for one iteration can significantly enhance the training effect of the model we proposed in this paper.We conduct the experiment by randomly cropped the same picture out as different parts for the input model to train,in which way can solve the problem that the dataset we collected is comparatively small,it improve the effect of classification obviously.
deep learning convolutional neural network image cropping
Qian Wang Cailan Zhou Ning Xu
School of Computer Science and Technology,Wuhan University of Technology Wuhan,China
国际会议
重庆
英文
1439-1443
2017-03-25（万方平台首次上网日期，不代表论文的发表时间）