Salient Object Detection by Optimizing Robust Background Detection

Abstract

Salient object detection aims to identify the most visually distinctive objects or regions in an image. Bottom-up methods always attract widespread attention in salient object detection. However, some bottom-up methods which based on background prior tend to outstand the salient object's edges rather than uniformly propagating the saliency value to the interior. In this paper, we present a Robust Background Detection Optimization (RBDO), which effectively improves an existing robust background detection method. This new method aims at the interior of salient objects which have uniform saliency values. First of all, a foreground probability map is constructed based on the background detection with intuitive geometrical interpretation. According to our proposed strategy, we get this point of the foreground probability map as a center point. Secondly, we combine center prior based on global contrast with the foreground probability, by which the accuracy of the foreground probability can be improved. New foreground probability is converted to background probability, and a weak saliency map can be obtained by background weighted contrast. Finally, we combine saliency optimization method with graph cut method to optimize saliency map together. Extensive experiments demonstrate that our algorithm performs favorably against state-of-the-art saliency detection methods on the four benchmark data sets.