  People may be unaware of the privacy risks of uploading an image online. In
this paper, we present Graph Privacy Advisor, an image privacy classifier that
uses scene information and object cardinality as cues to predict whether an
image is private. Graph Privacy Advisor simplifies a state-of-the-art graph
model and improves its performance by refining the relevance of the information
extracted from the image. We determine the most informative visual features to
be used for the privacy classification task and reduce the complexity of the
model by replacing high-dimensional image feature vectors with
lower-dimensional, more effective features. We also address the problem of
biased prior information by modelling object co-occurrences instead of the
frequency of object occurrences in each class.
