Publications
Authors:
  • Luca Marchesotti , Florent Perronnin
Citation:
24th British Machine Vision Conference (BMVC), University of Bristol, 9 - 13 Sept 2013. Full paper available on <a href=http://bmvc2013.bristol.ac.uk/> BMVC Website </a>
Abstract:
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.
Year:
2013
Report number:
2013/028