Abstract
Perception of a complex visual scene requires that important regions be prioritized and attentionally selected for processing. What is the basis for this selection? Although much research has focused on image salience as an important factor guiding attention, relatively little work has focused on semantic salience. To address this imbalance, we have recently developed a new method for measuring, representing, and evaluating the role of meaning in scenes. In this method, the spatial distribution of semantic features in a scene is represented as a meaning map. Meaning maps are generated from crowd-sourced responses given by naïve subjects who rate the meaningfulness of a large number of scene patches drawn from each scene. Meaning maps are coded in the same format as traditional image saliency maps, and therefore both types of maps can be directly evaluated against each other and against maps of the spatial distribution of attention derived from viewers’ eye fixations. In this review we describe our work focusing on comparing the influences of meaning and image salience on attentional guidance in real-world scenes across a variety of viewing tasks that we have investigated, including memorization, aesthetic judgment, scene description, and saliency search and judgment. Overall, we have found that both meaning and salience predict the spatial distribution of attention in a scene, but that when the correlation between meaning and salience is statistically controlled, only meaning uniquely accounts for variance in attention.
Highlights
The world contains an enormous amount of visual information, but human vision and visual cognition are severely limited in their processing capacity: Only a small fraction of the latent information can be analyzed at any given moment
The central idea of a with a saliency map?itTo address this we introduced a new approachinformativeness based on meaningover mapsa scene meaning map is that represents theissue, spatial distribution of semantic
We have reviewed the meaning map approach and shown how we have used it to investigate the relative roles of semantic informativeness and image salience on attentional guidance
Summary
The world contains an enormous amount of visual information, but human vision and visual cognition are severely limited in their processing capacity: Only a small fraction of the latent information can be analyzed at any given moment. Scene in that on input the detected and these predicted informativeness (i.e., meaning) of the sceneAdditionally, regions andthe objects image is the input for forming a map of potential attention targets For this model, the landscape [3,4]. Saliency map models naturally provide this typein of scenes prediction It is studies of meaning-based models of attention have typically focused on manipulations of far more difficult to create a computational model of meaning than it is to create a computational one or at most a small number of specific scene regions or objects [22,38,39,40,41].
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