Diffusion-based compactness is an effective method for foreground-based saliency detection, in which one key is the conventional graph construction. However, the conventional graph only displays the local structure but not preserves global relevance information. Therefore, diffusion-based compactness cannot highlight complete salient object which contains multiple areas with different features, and the extracted salient regions with weak homogeneous. Aiming to address these problems, we propose a saliency detection method via coarse-to-fine diffusion-based compactness with a weighted learning affinity matrix. Firstly, we construct multi-view conventional graphs to calculate the rough compactness cue. Secondly, we build a two-stage multi-view weighted graphs using a weighted learning affinity matrix and compute the coarse-to-fine compactness cue. Extensive experiments tested on three benchmark datasets, demonstrating the superior against several state-of-the-art methods.

基于扩散的紧密度是一种基于前景的显着性检测的有效方法，其中一个关键是常规图形构造。但是，常规图形仅显示局部结构，而没有保留全局相关性信息。因此，基于扩散的紧致度不能突出包含多个具有不同特征的区域的完整凸显对象，而提取的凸显区域具有较弱的同质性。为了解决这些问题，我们提出了一种基于加权的学习亲和度矩阵的，从粗糙到精细的基于扩散的紧度的显着性检测方法。首先，我们构造了多视图常规图以计算粗糙压实度提示。其次，我们使用加权学习亲和度矩阵构建了两阶段的多视图加权图，并计算了从粗糙到精细的紧密度提示。