The topological perception theory claims that visual perception of a scene begins from topological properties and then exploits local details. Inspired by this theory, we defined the topological descriptor and topological complexity, and we observed, based on statistics, that the saliencies of the regions with higher topological complexities are generally higher than those of regions with lower topological complexities. We then introduced the topological complexity as a saliency prior and proposed a novel unsupervised topo-prior-guided saliency detection system (TOPS). This system is framed as a topological saliency prior (topo-prior)-guided two-level local cue processing (i.e., pixel- and regional-level cues) with a multi-scale strategy, which includes three main modules: (1) a basic computational model of the topological perception theory for extracting topological features from images, (2) a topo-prior calculation method based on the topological features, and (3) a global–local saliency combination framework guided by the topo-prior. Extensive experiments on widely used salient object detection (SOD) datasets demonstrate that our system outperforms the unsupervised state-of-the-art algorithms. In addition, the topo-prior proposed in this work can be used to boost supervised methods including the deep-learning-based ones for fixation prediction and SOD tasks.

拓扑感知理论声称，场景的视觉感知从拓扑属性开始，然后利用局部细节。受此理论的启发，我们定义了拓扑描述符和拓扑复杂度，并根据统计数据观察到，拓扑复杂度较高的区域的显着性通常高于拓扑复杂度较低的区域的显着性。然后，我们将拓扑复杂性作为显着性进行了介绍，并提出了一种新颖的无监督的拓扑先导显着性检测系统（TOPS）。该系统被构造为具有多尺度策略的拓扑显着性先验（拓扑优先）引导的两级局部提示处理（即像素级和区域级提示），该策略包括三个主要模块：（1）用于从图像中提取拓扑特征的拓扑感知理论的基本计算模型；（2）基于拓扑特征的拓扑先验计算方法；以及（3）以拓扑为指导的全局-局部显着性组合框架事先的。在广泛使用的显着目标检测（SOD）数据集上的大量实验表明，我们的系统优于无人监督的最新算法。此外，这项工作中提出的拓扑先验可用于增强监督的方法，包括基于深度学习的注视预测和SOD任务方法。在广泛使用的显着目标检测（SOD）数据集上的大量实验表明，我们的系统优于无人监督的最新算法。此外，这项工作中提出的拓扑先验可用于增强监督的方法，包括基于深度学习的注视预测和SOD任务方法。在广泛使用的显着目标检测（SOD）数据集上的大量实验表明，我们的系统优于无人监督的最新算法。此外，这项工作中提出的拓扑先验可用于增强监督的方法，包括基于深度学习的注视预测和SOD任务方法。