  The style of an image plays a significant role in how it is viewed, but style
has received little attention in computer vision research. We describe an
approach to predicting style of images, and perform a thorough evaluation of
different image features for these tasks. We find that features learned in a
multi-layer network generally perform best -- even when trained with object
class (not style) labels. Our large-scale learning methods results in the best
published performance on an existing dataset of aesthetic ratings and
photographic style annotations. We present two novel datasets: 80K Flickr
photographs annotated with 20 curated style labels, and 85K paintings annotated
with 25 style/genre labels. Our approach shows excellent classification
performance on both datasets. We use the learned classifiers to extend
traditional tag-based image search to consider stylistic constraints, and
demonstrate cross-dataset understanding of style.
