  Object proposals greatly benefit object detection task in recent
state-of-the-art works. However, the existing object proposals usually have low
localization accuracy at high intersection over union threshold. To address it,
we apply saliency detection to each bounding box to improve their quality in
this paper. We first present a geodesic saliency detection method in contour,
which is designed to find closed contours. Then, we apply it to each candidate
box with multi-sizes, and refined boxes can be easily produced in the obtained
saliency maps which are further used to calculate saliency scores for proposal
ranking. Experiments on PASCAL VOC 2007 test dataset demonstrate the proposed
refinement approach can greatly improve existing models.
