基于域自适应的云南重彩画无监督情感识别

计算机视觉中，深度学习之所以取得如此巨大的突破，均得益于可获得的大规模标记数据集，而在图像情感分析中，由于情感语义的模糊性，导致图像情感标注困难，公开可获得图像情感数据集较少，其规模也小，制约了基于深度学习的图像情感分析性能。情感语义具有其特有的有序性和极性分组特征，而已有的图像情感分析方法中很少关注到情感语义的这些本质特征。基于域自适方法，考虑情感语义的本质特征，提出一种基于推土机距离的情感语义对齐方式，将带标记的情感数据集的训练模型更好地迁移到无标记的情感数据集上，实现无监督的图像情感分析，解决情感数据集标注困难的问题。该方法应用于创建的云南重彩画数据集，实验结果表明，其能有效地对齐源域和目标域数据，实现无监督的情感数据集自动标注，有利于扩充图像情感数据集规模。

Unsupervised emotion recognition of Yunnan Heavy Color Paintings based ondomain adaptation

Thanks to the large-scale labeled datasets available, deep learning has made a great breakthrough in computer vision. However, due to the ambiguity of emotion semantics, it is hard to annotate the emotional labels for images. Thus, only a few small-scale image emotion datasets are open and available, restricting the performance of image emotion analysis based on deep learning. The semantics of emotions have unique characteristics, such as order and polarity, but few studies have paid attention to these essential characteristics in image emotion analysis. Thus, in the paper, based on domain adaptation, considering the essential characteristics of emotion semantics, that is, the ordered and grouped polarity, we proposed to measure emotion semantic differences through earth mover’s distance (EMD). The goal is to better transfer the trained model with labeled emotion dataset to unlabeled emotion dataset and complete the unsupervised image emotion analysis. The Yunnan Heavy Color Paintings Emotion dataset was created in this paper, and was applied to our proposed method. The experimental results demonstrate that the proposed method can effectively align the emotional semantics between the source domain and the target domain, realizing the unsupervised automatic annotation of emotion dataset, thus expanding the size of the image emotion dataset.