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Learning Beautiful (and Ugly) attributes

Luca Marchesotti, Florent Perronnin
Current approaches to aesthetic image analysis either provide accurate or interpretable results. To get both accuracy and interpretability, we advocate the use of learned visual attributes as mid-level features. For this purpose, we propose to discover and learn the visual appearance of attributes automatically, using the recently introduced AVA database which contains more than 250,000 images together with their user ratings and textual comments. These learned attributes have many applications including aesthetic quality prediction, image classification and retrieval.
24th British Machine Vision Conference (BMVC), University of Bristol, 9 - 13 Sept 2013. Full paper available on BMVC Website