Luca Marchesotti, Naila Murray, Florent Perronnin
IEEE Conference on Computer Vision and Pattern Recognition, Providence, Rhode Island, USA, June 18-20, 2012.
With the ever-expanding volume of visual content available,
the ability to organize and navigate such content by
aesthetic preference is becoming increasingly important.
While still in its nascent stage, research into computational
models of aesthetic preference already shows great potential.
However, to advance research, realistic, diverse and
challenging databases are needed. To this end, we introduce
a new large-scale database for conducting Aesthetic
Visual Analysis: AVA. It contains over 250,000 images
along with a rich variety of meta-data including a
large number of aesthetic scores for each image, semantic
labels for over 60 categories as well as labels related to
photographic style. We show the advantages of AVA with respect
to existing databases in terms of scale, diversity, and
heterogeneity of annotations. We then describe several key
insights into aesthetic preference afforded by AVA. Finally,
we demonstrate, through three applications, how the large
scale of AVA can be leveraged to improve performance on
existing preference tasks.
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