Brain and Behavior Discovery Institute, Georgia Health Sciences University, Augusta, Georgia, United States of America.
PLoS One. 2010 Dec 29;5(12):e15796. doi: 10.1371/journal.pone.0015796.
Visual saliency is the perceptual quality that makes some items in visual scenes stand out from their immediate contexts. Visual saliency plays important roles in natural vision in that saliency can direct eye movements, deploy attention, and facilitate tasks like object detection and scene understanding. A central unsolved issue is: What features should be encoded in the early visual cortex for detecting salient features in natural scenes? To explore this important issue, we propose a hypothesis that visual saliency is based on efficient encoding of the probability distributions (PDs) of visual variables in specific contexts in natural scenes, referred to as context-mediated PDs in natural scenes. In this concept, computational units in the model of the early visual system do not act as feature detectors but rather as estimators of the context-mediated PDs of a full range of visual variables in natural scenes, which directly give rise to a measure of visual saliency of any input stimulus. To test this hypothesis, we developed a model of the context-mediated PDs in natural scenes using a modified algorithm for independent component analysis (ICA) and derived a measure of visual saliency based on these PDs estimated from a set of natural scenes. We demonstrated that visual saliency based on the context-mediated PDs in natural scenes effectively predicts human gaze in free-viewing of both static and dynamic natural scenes. This study suggests that the computation based on the context-mediated PDs of visual variables in natural scenes may underlie the neural mechanism in the early visual cortex for detecting salient features in natural scenes.
视觉显著性是指某些视觉场景中的项目从其直接上下文突出的感知质量。视觉显著性在自然视觉中起着重要的作用，因为它可以引导眼球运动、分配注意力，并促进物体检测和场景理解等任务。一个未解决的核心问题是：在早期视觉皮层中应该编码哪些特征来检测自然场景中的显著特征？为了探索这个重要的问题，我们提出了一个假设，即视觉显著性是基于对自然场景中特定上下文的视觉变量概率分布（PD）的有效编码，我们称之为自然场景中的上下文介导 PD。在这个概念中，早期视觉系统模型中的计算单元不作为特征检测器，而是作为自然场景中各种视觉变量的上下文介导 PD 的估计器，这直接产生了任何输入刺激的视觉显著性度量。为了验证这个假设，我们使用改进的独立成分分析（ICA）算法开发了一个自然场景中上下文介导 PD 的模型，并基于从一组自然场景中估计的这些 PD 得出了视觉显著性度量。我们证明了基于自然场景中上下文介导 PD 的视觉显著性能够有效地预测人类在静态和动态自然场景中的自由观看时的注视。这项研究表明，基于自然场景中视觉变量的上下文介导 PD 的计算可能是早期视觉皮层中检测自然场景中显著特征的神经机制的基础。
