基于图神经网络的东巴画小样本分类

Dongba Painting Few-Shot Classification Based on Graph Neural Network

针对纳西族东巴画艺术形象线条突出、色彩分明、样本较少的特点,提出一种端到端的基于图神经网络的东巴画小样本分类方法.首先,设计多分辨率多尺度的图像特征提取网络,图像特征与边缘特征融合后嵌入图神经网络中作为节点,构建分类图神经网络;其次,以边标记作为架构并采用二维边标记特征作为图像分类依据,保留节点分类时所需的类内相似性和类间相异性;最后,提出自注意力机制与特征显著性注意力机制相结合的方法更新节点特征,增强了节点之间的特征关联性.模型使用Python实现并用RTX2080Ti在自建东巴画数据集上进行实验,结果表明,所提方法较好地提取了东巴图像特征,保留了东巴画分类所需的图像局部细节和节点间相似性特征,与对比算法相比,提高了分类准确度,并有更低的分类精度标准差.

In view of the outstanding lines and distinguishing colors of the Naxi Dongba paintings,an endto-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 edgelabeling framework,the 2-dimensional edge feature is used as the image classification basis,and the intraclass 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 2080 Ti 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.