Modeling Visual Attention for Enhanced Image and Video Processing Applications

Abstract

Human attention is naturally drawn to visually salient or distinct stimuli. However, identifying all potentially interesting targets in a scene can be computationally complex. Visual saliency plays a crucial role in this process by highlighting important regions either through bottom-up (stimulus-driven) or top-down (goal-driven) mechanisms. In a bottom-up approach, attention is guided by inherent visual properties of the stimulus, whereas in a top-down approach, it is influenced by the user's intent or task. Over the past decade, researchers have developed various methods and models to detect visual distinctiveness in images and video frames. In this paper, we have discussed the visual attention modeling that has demonstrated wide-ranging applications, including image and video quality assessment, video summarization (such as video skimming and key frame extraction), and more.