  A natural approach to teaching a visual concept, e.g. a bird species, is to
show relevant images. However, not all relevant images represent a concept
equally well. In other words, they are not necessarily iconic. This observation
raises three questions. Is iconicity a subjective property? If not, can we
predict iconicity? And what exactly makes an image iconic? We provide answers
to these questions through an extensive experimental study on a challenging
fine-grained dataset of birds. We first show that iconicity ratings are
consistent across individuals, even when they are not domain experts, thus
demonstrating that iconicity is not purely subjective. We then consider an
exhaustive list of properties that are intuitively related to iconicity and
measure their correlation with these iconicity ratings. We combine them to
predict iconicity of new unseen images. We also propose a direct iconicity
predictor that is discriminatively trained with iconicity ratings. By combining
both systems, we get an iconicity prediction that approaches human performance.
