Milojevic Zarko, Ennis Robert, Toscani Matteo, Gegenfurtner Karl R
Abteilung Allgemeine Psychologie, Justus Liebig University Giessen, Otto-Behagel-Str. 10F, D-35394 Giessen, Germany.
Abteilung Allgemeine Psychologie, Justus Liebig University Giessen, Otto-Behagel-Str. 10F, D-35394 Giessen, Germany.
Vision Res. 2018 Oct;151:18-30. doi: 10.1016/j.visres.2018.01.008. Epub 2018 Mar 23.
The natural objects that we are surrounded with virtually always contain many different shades of color, yet the visual system usually categorizes them into a single color category. We examined various image statistics and their role in categorizing the color of leaves. Our subjects categorized photographs of autumn leaves and versions that were manipulated, including: randomly repositioned pixels, leaves uniformly colored with their mean color, leaves that were made by reflecting the original leaves' chromaticity distribution about their mean ("flipped leaves"), and simple patches colored with the mean colors of the original leaves. We trained a linear classifier with a set of image statistics in order to predict the category that each object was assigned to. Our results show that the mean hue of an object is highly predictive of the natural object's color category (>90% accuracy) and observers' choices are consistent with their use of unique yellow as a decision boundary for classification. The flipped leaves produced consistent changes in color categorization that are possibly explained by an interaction between the color distributions and the texture of the leaves.
我们周围的自然物体几乎总是包含许多不同的颜色深浅，但视觉系统通常会将它们归类为单一的颜色类别。我们研究了各种图像统计数据及其在树叶颜色分类中的作用。我们的受试者对秋叶照片以及经过处理的版本进行了分类，这些版本包括：随机重新定位像素、用平均颜色均匀上色的树叶、通过将原始树叶的色度分布围绕其均值进行反射而制成的树叶（“翻转树叶”），以及用原始树叶的平均颜色上色的简单色块。我们使用一组图像统计数据训练了一个线性分类器，以预测每个物体被分配到的类别。我们的结果表明，物体的平均色调对自然物体的颜色类别具有高度预测性（准确率>90%），并且观察者的选择与他们使用独特的黄色作为分类的决策边界一致。翻转树叶在颜色分类中产生了一致的变化，这可能是由颜色分布和树叶纹理之间的相互作用所解释的。
