Emotional valence categorization using holistic image features

Can a machine learn to perceive emotions as evoked by an artwork? Here we propose an emotion categorization system, trained by ground truth from psychology studies. The training data contains emotional valences scored by human subjects on the International Affective Picture System (IAPS), a standard emotion evoking image set in psychology. Our approach is based on the assessment of local image statistics which are learned per emotional category using support vector machines. We show results for our system on the I APS dataset, and for a collection of masterpieces. Although the results are preliminary, they demonstrate the potential of machines to elicit realistic emotions when considering masterpieces.