  In this paper, we present a subclass-representation approach that predicts
the probability of a social image belonging to one particular class. We explore
the co-occurrence of user-contributed tags to find subclasses with a strong
connection to the top level class. We then project each image on to the
resulting subclass space to generate a subclass representation for the image.
The novelty of the approach is that subclass representations make use of not
only the content of the photos themselves, but also information on the
co-occurrence of their tags, which determines membership in both subclasses and
top-level classes. The novelty is also that the images are classified into
smaller classes, which have a chance of being more visually stable and easier
to model. These subclasses are used as a latent space and images are
represented in this space by their probability of relatedness to all of the
subclasses. In contrast to approaches directly modeling each top-level class
based on the image content, the proposed method can exploit more information
for visually diverse classes. The approach is evaluated on a set of $2$ million
photos with 10 classes, released by the Multimedia 2013 Yahoo! Large-scale
Flickr-tag Image Classification Grand Challenge. Experiments show that the
proposed system delivers sound performance for visually diverse classes
compared with methods that directly model top classes.
