A Functional and Statistical Bottom-Up Saliency Model to Reveal the Relative Contributions of Low-Level Visual Guiding Factors
A Functional and Statistical Bottom-Up Saliency Model to Reveal the Relative Contributions of Low-Level Visual Guiding Factors
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DOI：
10.1007/s12559-010-9078-8
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发表时间：
2010-12-01
影响因子：
5.4
通讯作者：
Guerin-Dugue, Anne
中科院分区：
文献类型：
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作者：
Ho-Phuoc, Tien;Guyader, Nathalie;Guerin-Dugue, Anne
当我们看一个场景时，我们经常移动眼睛，在视网膜中央凹上放置连续的有趣区域。在每次注视时，视觉系统只详细分析这个特定的中央凹区域。视觉注意机制控制眼球运动，并取决于两种类型的因素：自下而上和自上而下的因素。自下而上的因素包括不同的视觉特征，如颜色、亮度、边缘和方向。在本文中，我们定量评估的相对贡献的基本低级别的功能作为候选人的指导因素的视觉注意，从而眼动。我们还研究了如何将这些视觉特征结合在一个自下而上的显着性模型中。我们的工作包括三个交互部分：一个功能显着性模型，一个统计模型和眼动数据记录在自由观看自然场景。受灵长类视觉系统的启发，功能显着性模型将视觉场景分解为不同的特征图。统计模型表明哪些特征最能解释记录的眼球运动。我们显示了一个重要的作用，高频率的亮度和中央固定偏置的重要贡献。然后，使用由统计模型计算的特征的相对贡献来将不同的特征图联合收割机组合成显著性图。最后，显着性模型和实验数据之间的比较证实了这些贡献的影响。
When looking at a scene, we frequently move our eyes to place consecutive interesting regions on the fovea, the retina centre. At each fixation, only this specific foveal region is analysed in detail by the visual system. The visual attention mechanisms control eye movements and depend on two types of factor: bottom-up and top-down factors. Bottom-up factors include different visual features such as colour, luminance, edges, and orientations. In this paper, we evaluate quantitatively the relative contribution of basic low-level features as candidate guiding factors to visual attention and hence to eye movements. We also study how these visual features can be combined in a bottom-up saliency model. Our work consists of three interactive parts: a functional saliency model, a statistical model and eye movement data recorded during free viewing of natural scenes. The functional saliency model, inspired by the primate visual system, decomposes a visual scene into different feature maps. The statistical model indicates which features best explain the recorded eye movements. We show an essential role of high frequency luminance and an important contribution of central fixation bias. The relative contribution of features, calculated by the statistical model, is then used to combine the different feature maps into a saliency map. Finally, the comparison between the saliency model and experimental data confirmed the influence of these contributions.