Abstract: Images are powerful tools with which to convey human emotions, with different images stimulating diverse emotions. In this paper, a data augmentation method based on small data sets is adopted. The advanced features （object category and deep emotion feature） are used, which are automatically extracted by the deep network combined with low-level features （color and texture features） to recognize emotion of the image. A high-level semantic descriptive phrase including compound emotions and object is output. The results show that the proposed method is superior to other traditional manual extraction methods or existing deep learning models and achieves 66.54% accuracy on emotion recognition on IAPS and GAPED data sets.

Key words: image semantics, transfer learning, Convolutional Neural Network（CNN）

摘要： 图像是一种用来传达情感的重要工具，人类的情感会因不同的视觉刺激而异。采用了一种基于小数据集的数据扩充方式，并将图像的手工提取的低级特征（颜色特征、纹理特征）和网络自动提取到的高级特征（图像对象类别特征和图像深层情感特征）融合的方法，识别图像的复合情感。最终输出包含图像和对象在内的高级语义描述性短语。在公共数据集IAPS和GAPED上进行了实验，并与传统手工提取方法和VGG16、Fine-tune Alexnet两种已有模型进行了比较，该方法在测试性能上优于其他的识别方法，情感识别准确率能达到66.54%。

关键词: 图像情感, 迁移学习, 卷积神经网络