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 computer vision and multimedia. Current researches usually consider the relationship model between images and emotions as a black box. They extract the traditional discursive visual features such as SIFT and wavelet textures, and use them directly 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 classification accuracies on these visual features are not satisfactory for a long time. We propose the interpretable aesthetic features to describe images inspired by art theories, which are intuitive, discriminative and easily understandable. Affective image classification based on these features can achieve higher 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.