Categorizing natural color distributions
Authors: Milojevic, Z., Ennis, R., Toscani, M. and Gegenfurtner, K.R.
Journal: Vision Research
Volume: 151
Pages: 18-30
eISSN: 1878-5646
ISSN: 0042-6989
DOI: 10.1016/j.visres.2018.01.008
Abstract: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.
Source: Scopus
Categorizing natural color distributions.
Authors: Milojevic, Z., Ennis, R., Toscani, M. and Gegenfurtner, K.R.
Journal: Vision Res
Volume: 151
Pages: 18-30
eISSN: 1878-5646
DOI: 10.1016/j.visres.2018.01.008
Abstract: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.
Source: PubMed
Categorizing natural color distributions
Authors: Milojevic, Z., Ennis, R., Toscani, M. and Gegenfurtner, K.R.
Journal: VISION RESEARCH
Volume: 151
Pages: 18-30
eISSN: 1878-5646
ISSN: 0042-6989
DOI: 10.1016/j.visres.2018.01.008
Source: Web of Science (Lite)
Categorizing natural color distributions.
Authors: Milojevic, Z., Ennis, R., Toscani, M. and Gegenfurtner, K.R.
Journal: Vision research
Volume: 151
Pages: 18-30
eISSN: 1878-5646
ISSN: 0042-6989
DOI: 10.1016/j.visres.2018.01.008
Abstract: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.
Source: Europe PubMed Central