Dongba Painting Few-Shot Classification Based on Graph Neural Network

In view of the outstanding lines and distinguishing colors of the Naxi Dongba paintings, an end- to-end few-shot classification method based on graph neural network is proposed. Firstly, a multi-resolution and multi-scale image feature extraction network is designed, the image feature and edge feature are fused and embedded into the graph as nodes to construct classification graph neural network. Secondly, with edge- labeling framework, the 2-dimensional edge feature is used as the image classification basis, and the intra- class similarity and interclass dissimilarity required for node classification are preserved. Finally, a method combining self-attention and feature saliency attention mechanism is proposed to update the node features and enhance the feature correlation between nodes. The model is implemented by Python, and RTX 2080Ti is used to conduct classification experiments on the self-built Dongba painting few-shot dataset. The experimental results show that the method can better extract the features of Dongba painting image and more sufficiently retain local details and structural features required for image classification of Dongba painting. The accuracy and standard deviation of the classification are better than others on the few-shot dataset of Dongba painting image.