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