  Large-scale image retrieval benchmarks invariably consist of images from the
Web. Many of these benchmarks are derived from online photo sharing networks,
like Flickr, which in addition to hosting images also provide a highly
interactive social community. Such communities generate rich metadata that can
naturally be harnessed for image classification and retrieval. Here we study
four popular benchmark datasets, extending them with social-network metadata,
such as the groups to which each image belongs, the comment thread associated
with the image, who uploaded it, their location, and their network of friends.
Since these types of data are inherently relational, we propose a model that
explicitly accounts for the interdependencies between images sharing common
properties. We model the task as a binary labeling problem on a network, and
use structured learning techniques to learn model parameters. We find that
social-network metadata are useful in a variety of classification tasks, in
many cases outperforming methods based on image content.
