Graph-based salient object detection methods have gained more and more attention recently. However, existing works fail to separate effectively salient object and background in some challenging scenes. Inspired by this observation, we propose an effective salient object detection method based on a novel boundary-guided graph structure. More specifically, the input image is firstly segmented into a series of superpixels. Then we integrate two prior cues to generate the coarse saliency map, a novel weighting mechanism is proposed to balance the proportion of two prior cues according to their performance. Secondly, we propose a novel boundary-guided graph structure to explore deeply the intrinsic relevance between superpixels. Based on the proposed graph structure, an iterative propagation mechanism is constructed to refine the coarse saliency map. Experimental results on four datasets show adequately the superiority of the proposed method than other state-of-the-art methods.

基于图的显着目标检测方法近来受到越来越多的关注。但是，现有的作品无法在某些具有挑战性的场景中有效地区分出显着的物体和背景。受此启发，我们提出了一种基于新型边界引导图结构的有效显着目标检测方法。更具体地，首先将输入图像分割成一系列的超像素。然后，我们整合两个先验线索，以生成粗糙显着性图，提出了一种新颖的加权机制，以根据其性能来平衡两个先验线索的比例。其次，我们提出了一种新颖的边界引导图结构，以深入探索超像素之间的内在关联性。基于所提出的图结构，构造了迭代传播机制以细化粗糙显着图。