Interpretable aesthetic features for affective image classification

Images can not only display contents themselves, but also convey emotions, e.g., excitement, sadness. Affective image classification is useful and hot in many fields such as comput-er vision and multimedia. Current researches usually consid-er the relationship model between images and emotions as a black box. They extract the traditional discursive visual fea-tures such as SIFT and wavelet textures, and use them di-rectly upon various classification algorithms. However, these visual features are not interpretable, and people cannot know why such a set of features induce a particular emotion. And due to the highly subjective nature of images, the classifica-tion accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic fea-tures to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve high-er accuracy, compared with the state-of-the-art. Specifically, the features can also intuitively explain why an image tends to convey a certain emotion. We also develop an emotion guided image gallery to demonstrate the proposed feature collection. Index Terms — image features, affective classification, interpretability, art theory 1