Due to their intrinsic capabilities in capturing, modeling, and representing relationships between pieces of information, graphs have been used in a wide variety of application domains. Surprisingly, over the past two decades, graphs in general-and graph-based algorithms in particular—have been employed for a challenging computer vision task: saliency detection. More specifically, researchers have explored ways to model saliency in images using graph structures, graph manifolds, and neural networks to identify the most significant and attention-grabbing regions and objects in a visual scene. From another perspective, saliency in the context of graphs refers to nodes and edges (i.e., subgraphs) that hold greater importance, such as those exhibiting stronger connectivity or structural significance. This early study explores underexamined topics and introduces a novel link between node saliency in graphs and salient objects in images. Promising results suggest the potential to inspire new research on using graphs in computer vision and pattern recognition.
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