基于注意力机制的东巴画情感分类

Learning attention for Dongba paintings emotion classification

东巴画情感多元、样本较少,基于注意力机制的分类算法能有效辅助东巴画情感分类,解决东巴画样本较少的问题。首先,按照绘画主题,将东巴画分为人物、鬼怪、动物、植物4类题材,按照绘画情感分为勤劳朴实、曼妙美好等12种情感;其次,采用编码器-解码器架构实现东巴画的情感特征提取,引入预训练模型提升分类模型的泛化性能,加速小样本东巴画情感分类收敛;最后,在解码器中设置空白注意力并融合编码器的输出序列,经由解码器的解码学习机制,学习东巴画图像语义以指导模型更准确、合理地提升对东巴画情感的学习能力。实验结果表明,基于注意力机制的分类算法相较现有方法分类准确率更高,获得了80.7%的分类准确率,解决了东巴画情感丰富、难以区分的问题。

Rich in emotions and limited in samples constitute the artistic characteristic of Dongba paintings. The classification algorithm of learning attention could effectively assist the emotional classification of Dongba paintings,solving the problem of limited samples of Dongba paintings. Firstly, Dongba paintings were divided into 4 themes such as figures, ghosts, animals, and plants. According to the painting emotions, Dongba paintings were divided into 12 kinds of emotions, such as industriousness, simplicity, grace, and beauty. Secondly, the architecture of encoder and decoder was employed to extract their emotional features, while the pre-training model was used to improve the generalization performance of the classification model and accelerate the convergence of emotion classification for small samples of Dongba paintings. Finally, the blank attention was set in the decoder and the output sequence of the encoder was fused. Through the decoder, the semantics of Dongba paintings were learned, guiding the model to more accurately and reasonably improve the classification ability. Experiments show that the classification algorithm of learning attention could attain a classification accuracy of 80.7% higher than the existing methods, solving the problem of rich and difficult-to-distinguish emotions in Dongba paintings.