Predicting discrete probability distribution of image emotions

Most existing works on affective image classification tried to assign a dominant emotion category to an image. However, this is often insufficient, as the emotions that are evoked in viewers by an image are highly subjective and different. In this paper, we propose to predict the probability distribution of categorical image emotions. Firstly we extract commonly used features of different levels for each image. Then we formulize the emotion distribution prediction as a shared sparse leaning problem, which is optimized by iteratively reweighted least squares. Besides, we introduce three baseline algorithms. Experiments are carried out on a dataset of peer rated abstract paintings and the results demonstrate the superiority of our proposed method, as compared to some state-of-the-art approaches.