  One of the fundamental properties of a salient object region is its contrast
with the immediate context. The problem is that numerous object regions exist
which potentially can all be salient. One way to prevent an exhaustive search
over all object regions is by using object proposal algorithms. These return a
limited set of regions which are most likely to contain an object. Several
saliency estimation methods have used object proposals. However, they focus on
the saliency of the proposal only, and the importance of its immediate context
has not been evaluated.
  In this paper, we aim to improve salient object detection. Therefore, we
extend object proposal methods with context proposals, which allow to
incorporate the immediate context in the saliency computation. We propose
several saliency features which are computed from the context proposals. In the
experiments, we evaluate five object proposal methods for the task of saliency
segmentation, and find that Multiscale Combinatorial Grouping outperforms the
others. Furthermore, experiments show that the proposed context features
improve performance, and that our method matches results on the FT datasets and
obtains competitive results on three other datasets (PASCAL-S, MSRA-B and
ECSSD).
