Image Classification Based on transfer Learning of Convolutional neural network
Yunyan Wang, Chongyang Wang, Lengkun Luo, Zhigang Zhou · 2019
Aiming at the issue of timeliness and lack of partial image data in life, an algorithm, transfer learning which based on convolutional neural network (CNN) is proposed, combining image histogram of oriented gradient (HOG) feature extraction method and support vector machine (SVM) pre-classification method. Firstly, the HOG features of the training sample similar to the attributes of the samples which to be classified are extracted, then the hog features of the training samples are imported into the SVM classifier to get the pre-classification results. Finally, the pre-classification results are used as training samples to train the transfer network of CNN for getting new transfer learning model, this model can be used to classify similar pre-classification samples. The experimental results show that the classification accuracy of the five categories of elephants and dinosaurs used in this paper is effectively improved, and the overall classification accuracy can reach 95%, compared with the traditional classifier algorithm and convolutional neural network algorithm. The classification accuracy has been improved by about 5%.