  Salient object detection has become an important task in many image
processing applications. The existing approaches exploit background prior and
contrast prior to attain state of the art results. In this paper, instead of
using background cues, we estimate the foreground regions in an image using
objectness proposals and utilize it to obtain smooth and accurate saliency
maps. We propose a novel saliency measure called `foreground connectivity'
which determines how tightly a pixel or a region is connected to the estimated
foreground. We use the values assigned by this measure as foreground weights
and integrate these in an optimization framework to obtain the final saliency
maps. We extensively evaluate the proposed approach on two benchmark databases
and demonstrate that the results obtained are better than the existing state of
the art approaches.
