To address the misjudgment caused by all boundaries of an image being equally and artificially selected as background in most of state-of-the-art models using background prior

this paper proposes an algorithm called weighted contrast optimization based on discriminative background.Firstly

a metric is constructed to roughly but objectively estimate a saliency map

which is used to choose a better background map.Based on this metric

a reliable background detection model is constructed through geodesic distance transformation after discriminating each boundary via Hausdorff distance.Then

the only background weighted contrast is improved into fore-background weighted contrast.Last

the final saliency map is obtained through weighted optimization framework.Extensive experiments on five public datasets demonstrate that the proposed algorithm outperforms state-of-the-art methods.