  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.
