低级特征预测了对自然主义图像的感知相似性
Emily J A-Izzeddin1,2,3,4, Thomas S A Wallis3,5,6, Jason B Mattingley2,7,8
1Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany.
Journal of vision
|October 7, 2025
概括
人类的视觉感知利用低级图像特征组织场景. 像亮度和结构相似度这样的基本特征有助于关联不同的图像区域,即使在复杂的场景中.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 视觉感知 视觉感知 视觉感知
背景情况:
- 人类视觉场景组织的机制尚未完全理解.
- 使用简单的刺激和低级图像特征来研究统计规律的感知.
- 存在计算模型来解释基于低级图像特征的感知.
研究的目的:
- 调查观察者如何在自然主义场景区域中使用低级信息来结合.
- 确定基本图像特征在多大程度上促进了区域关联.
- 探索亮度和结构相似性在场景感知中的作用.
主要方法:
- 使用两种替代性强制选择方法的两项实验.
- 参与者判断图像补丁是否属于同一场景.
- 分析包括一般化的线性多层模型与低级特征相似性预测器.
主要成果:
- 参与者成功地使用未经改变的补丁将随机以上的图像区域关联起来.
- 低级特征相似度指标 (亮度,结构相关性) 预测了响应.
- 与值或边缘图像区域的性能下降,但模型仍然相关.
结论:
- 图像区域的关联可以通过低级特征相关性来解释.
- 基本的视觉特征与对复杂视觉刺激的判断密切相关.
- 低级图像属性在连贯的场景表示中起着至关重要的作用.
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