Recognizing Image Style
This work addresses the challenge of recognizing image style for applications like enhanced image search and cross-dataset understanding, representing a novel domain-specific contribution.
The paper tackles the problem of predicting image style, which had received little attention in computer vision, by developing an approach using multi-layer network features trained on object class labels, achieving the best published performance on an existing dataset and excellent classification on two new large-scale datasets.
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.