Abstract: Painting image sentiment prediction is a research hotspot in affective computing. At present, there are few sources of abstract paintings and a small sample size; most of its sentiment analysis uses low-level features of the image, and the accuracy is not high. To resolve these problems, a sentiment prediction of small sample abstract painting image based on feature fusion was proposed. First, the relationship between the basic elements of abstract painting (point, line, plane and color) and human emotions in abstract art theory was analyzed, and according to these theories, the low-level features of abstract painting image were quantified. Second, the transfer learning algorithm was adopted to obtain the parameters from large sample data in the pre-training network, and these parameters were transferred to the target model, and then the target model was fine-tuned on the small sample data to obtain the high-level features of the image. Finally, the low-level and high-level features were fused linearly, and the multi-class Support Vector Machine (SVM) was used to achieve the sentiment prediction of abstract painting image. The experiments were carried out on three small sample abstract painting datasets, and the proposed method was compared with the methods of directly using low-level features. The results show that the classification accuracy of the proposed algorithm is improved, confirming its effectiveness in sentiment research of small sample abstract painting.

Key words: feature fusion, sentiment prediction, abstract art theory, transfer learning, small sample

摘要： 绘画图像情感预测是目前情感计算中的一个研究热点。目前抽象画来源少，样本量小，其情感分析大多数采用的是图像低层特征，而且准确率不高。为此，提出一种基于特征融合的小样本抽象画图像情感预测方法。首先，分析了抽象艺术理论中组成绘抽象画的基本元素（点、线、面和颜色）与人类情感的关系，依据这些理论量化出抽象画图像的低层特征；然后，采用迁移学习算法，基于大样本数据在预训练网络上得到参数，并迁移至目标模型，再在小样本数据上对目标模型进行微调，得到图像高层特征；最后，将低层与高层特征进行线性融合，采用多分类支持向量机（SVM）实现抽象画图像的情感预测。在3个小样本抽象画数据集上进行实验，结果表明，与直接采用低层特征的方法相比，所提方法的分类准确率有所提高，证实了它在小样本抽象画的情感研究中的有效性。

关键词: 特征融合, 情感预测, 抽象艺术理论, 迁移学习, 小样本