  Context modeling is one of the most fertile subfields of visual recognition
which aims at designing discriminant image representations while incorporating
their intrinsic and extrinsic relationships. However, the potential of context
modeling is currently underexplored and most of the existing solutions are
either context-free or restricted to simple handcrafted geometric
relationships. We introduce in this paper DHCN: a novel Deep Hierarchical
Context Network that leverages different sources of contexts including
geometric and semantic relationships. The proposed method is based on the
minimization of an objective function mixing a fidelity term, a context
criterion and a regularizer. The solution of this objective function defines
the architecture of a bi-level hierarchical context network; the first level of
this network captures scene geometry while the second one corresponds to
semantic relationships. We solve this representation learning problem by
training its underlying deep network whose parameters correspond to the most
influencing bi-level contextual relationships and we evaluate its performances
on image annotation using the challenging ImageCLEF benchmark.
