# Estimating Color-Concept Associations from Image Statistics

## TL;DR

This paper presents an automated method to estimate human-like color-concept associations from Google Image search results, aiding in the design of semantically interpretable color palettes for visualizations.

## Contribution

The authors introduce a novel approach that extracts color-concept associations directly from image data without human input, improving efficiency and scalability.

## Key findings

- Accurately estimates color-concept associations for fruits using few images.
- Method correlates strongly with human ratings.
- Generalizes moderately well to complex concepts.

## Abstract

To interpret the meanings of colors in visualizations of categorical information, people must determine how distinct colors correspond to different concepts. This process is easier when assignments between colors and concepts in visualizations match people's expectations, making color palettes semantically interpretable. Efforts have been underway to optimize color palette design for semantic interpretablity, but this requires having good estimates of human color-concept associations. Obtaining these data from humans is costly, which motivates the need for automated methods. We developed and evaluated a new method for automatically estimating color-concept associations in a way that strongly correlates with human ratings. Building on prior studies using Google Images, our approach operates directly on Google Image search results without the need for humans in the loop. Specifically, we evaluated several methods for extracting raw pixel content of the images in order to best estimate color-concept associations obtained from human ratings. The most effective method extracted colors using a combination of cylindrical sectors and color categories in color space. We demonstrate that our approach can accurately estimate average human color-concept associations for different fruits using only a small set of images. The approach also generalizes moderately well to more complicated recycling-related concepts of objects that can appear in any color.