The Good, the Bad, and the Ugly: Predicting Aesthetic Image Labels
Automatic classification of the aesthetic content of a picture is one of the challenges in the emerging discipline of computational aesthetics. Any suitable solution must cope with the facts that aesthetic experiences are highly subjective and that a commonly agreed upon theory of their psychological constituents is still missing. In this paper, we present results obtained from an empirical basis of several thousand images. We train SVM based classifiers to predict aesthetic adjectives rather than aesthetic scores and we introduce a probabilistic post processing step that alleviates effects due to misleadingly labeled training data. Extensive experimentation indicates that aesthetics classification is possible to a large extent. In particular, we find that previously established low-level features are well suited to recognize beauty. Robust recognition of unseemliness, on the other hand, appears to require more high-level analysis.