Affective Image Classiﬁcation using Features Inspired by Psychology and Art Theory
Place: Large Lecture Room - CVC
Images can aﬀect people on an emotional level. Since the emotions that arise in the viewer of an image are highly subjective, they are rarely indexed. However there are situations when it would be helpful if images could be retrieved based on their emotional content. We investigate and develop methods to extract and combine low-level features that represent the emotional content of an image, and use these for image emotion classiﬁcation. Speciﬁcally, we exploit theoretical and empirical concepts from psychology and art theory to extract image features that are speciﬁc to the domain of artworks with emotional expression. For testing and training, we use three data sets: the International Affective Picture System (IAPS); a set of artistic photography from a photo sharing site (to investigate whether the conscious use of colors and textures displayed by the artists improves the classiﬁcation); and a set of peer rated abstract paintings to investigate the inﬂuence of the features and ratings on pictures without contextual content. Improved classiﬁcation results are obtained on the International Aﬀective Picture System (IAPS), compared to state of the art work.