基于自适应图的半监督图像分类方法

To solve the problems of higher model complexity and difficulty in constructing regularization items in the semi-supervised classification model, a new semi-supervised image classification model named AGSH from the perspective of enriching sample feature representation is constructed, which is fusion with an adaptive graph structure. The model AGSH introduces the adaptive graph convolutional neural network AGCN, aiming to extract the relationship between the features of the CNN model based on the convolutional neural network model CNN. The analysis of the generalization performance of the AGSH model also shows the effectiveness of solving semi-supervised related problems. The experimental results show that the accuracy of the AGSH model is improved compared with that of the single CNN model on the five image datasets. The research expands the content of the semi-supervised image classification algorithm and provides an essential reference for the modeling method to solve the few-sample classification problem.