Low-level features predict perceived similarity for naturalistic images.

IF 2.3 4区 心理学 Q2 OPHTHALMOLOGY
Emily J A-Izzeddin, Thomas S A Wallis, Jason B Mattingley, William J Harrison
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引用次数: 0

Abstract

The mechanisms by which humans perceptually organize individual regions of a visual scene to generate a coherent scene representation remain largely unknown. Our perception of statistical regularities has been relatively well-studied in simple stimuli, and explicit computational mechanisms that use low-level image features (e.g., luminance, contrast energy) to explain these perceptions have been described. Here, we investigate to what extent observers can effectively use such low-level information present in isolated naturalistic scene regions to facilitate associations between said regions. Across two experiments, participants were shown an isolated reference patch, then required to select which of two subsequently presented patches came from the same scene as the reference (two-alternative forced choice method). In Experiment 1, participants made their judgments based on unaltered image patches, and were consistently above chance when performing such association judgments. Additionally, participants' responses were well-predicted by a generalized linear multilevel model using predictors based on low-level feature similarity metrics (specifically, pixel-wise luminance and phase-invariant structure correlations). In Experiment 2, participants were presented with unaltered image regions, thresholded image regions, or regions reduced to only their edge content. Performance for thresholded and edge regions was significantly poorer than for unaltered image regions. Nonetheless, the model still correlated well with participants' judgments. Our findings suggest that image region associations can be accounted for using low-level feature correlations, suggesting such basic features are strongly associated with those underlying judgments made for complex visual stimuli.

低级特征预测自然图像的感知相似性。
人类通过感知组织视觉场景的各个区域以产生连贯的场景表示的机制在很大程度上仍然未知。我们对统计规律的感知已经在简单刺激中得到了相对较好的研究,并且已经描述了使用低级图像特征(例如亮度,对比能量)来解释这些感知的明确计算机制。在这里,我们研究了观察者在多大程度上可以有效地利用孤立的自然场景区域中存在的低水平信息来促进所述区域之间的联系。在两个实验中,参与者被展示了一个孤立的参考斑块,然后被要求在随后呈现的两个斑块中选择哪个来自与参考相同的场景(双选项强制选择方法)。在实验1中,参与者根据未改变的图像块进行判断,并且在进行这种关联判断时始终高于机会。此外,参与者的反应可以通过使用基于低水平特征相似性度量(特别是像素亮度和相位不变结构相关性)的预测因子的广义线性多层模型进行很好的预测。在实验2中,参与者被呈现未改变的图像区域,阈值图像区域,或仅减少到其边缘内容的区域。阈值和边缘区域的性能明显低于未改变的图像区域。尽管如此,该模型仍然与参与者的判断密切相关。我们的研究结果表明,图像区域关联可以用低水平的特征相关性来解释,这表明这些基本特征与对复杂视觉刺激做出的潜在判断密切相关。
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来源期刊
Journal of Vision
Journal of Vision 医学-眼科学
CiteScore
2.90
自引率
5.60%
发文量
218
审稿时长
3-6 weeks
期刊介绍: Exploring all aspects of biological visual function, including spatial vision, perception, low vision, color vision and more, spanning the fields of neuroscience, psychology and psychophysics.
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