Dongba Painting Few-shot Classification Combining Spatial Information and Distribution Relationship

As an important part of national culture, Naxi Dongba painting has the artistic characteristics of rich texture and bright colors. However, due to the scarcity and complexity of Dongba painting, the existing deep learning methods perform poorly on the classification task. To improve this situation, we propose a few-shot classification method that combines spatial information and distribution relationship. Firstly, a pair of independent encoding operations are used to aggregate the spatial information into the channel attention, and the attention feature maps with orientation-aware and position-sensitive are obtained to accurately locate key regions. Secondly, feed the feature maps into the adaptive meta-weight generator to enhance the expressive ability of the features. Finally, a dual complete graph neural network is constructed to explicitly use the sample instance information and distribution relationship to perform classification prediction. Model training on 3 self-built Dongba painting datasets. Compared with other few-shot methods, the classification accuracy of the proposed method is improved by 9.79%, 5.76% and 8.21% respectively. The experimental results show that the method can more effectively exact the global information and detailed features of samples required for the classification of Dongba painting, and it can be effectively applied to the protection and inheritance of minority cultures.