Meaning and Attentional Guidance in Scenes: A Review of the Meaning Map Approach.
Meaning and Attentional Guidance in Scenes: A Review of the Meaning Map Approach.
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DOI：
10.3390/vision3020019
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发表时间：
2019-05-10
期刊：
影响因子：
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通讯作者：
Rehrig, Gwendolyn
中科院分区：
文献类型：
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作者：
Henderson, John M;Hayes, Taylor R;Rehrig, Gwendolyn
对复杂视觉场景的感知需要对重要的区域进行优先排序和注意选择进行处理。这种选择的依据是什么？尽管许多研究都将图像显著性作为引导注意力的重要因素，但对语义显著性的研究相对较少。为了解决这种不平衡，我们最近开发了一种新的方法来测量、表示和评估场景中意义的作用。该方法将场景中语义特征的空间分布表示为意义图。意义图是由天真的受试者给出的群众反馈生成的，他们对从每个场景绘制的大量场景补丁的意义进行评分。意义图的编码格式与传统图像显著性图相同，因此这两种类型的地图可以直接相互评估，也可以根据观看者眼睛注视产生的注意力空间分布地图进行评估。在这篇综述中，我们描述了我们的工作重点是比较意义和图像显著性对现实世界场景中注意力引导的影响，我们研究了各种观看任务，包括记忆、审美判断、场景描述和显著性搜索和判断。总的来说，我们发现意义和显著性都能预测一个场景中注意力的空间分布，但当意义和显著性之间的相关性得到统计控制时，只有意义能唯一地解释注意力的变化。
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 naive 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.