Proceedings Article | 25 January 2012
KEYWORDS: Data processing, Databases, Clouds, RGB color model, Color vision, Mobile devices, Computing systems, Nomenclature, Data modeling, Solids
There is a very long tradition in designing color palettes for various applications, going back to at least the Upanishad.
Although color palettes have been influenced by the available colorants, starting with the advent of aniline dyes in the late
1850s there have been few physical limits on the choice of individual colors. This abundance of choices exacerbates the
problem of limiting the number of colors in a palette, i.e., in keeping them into a manageable quantity.
For example, it is not practical for a car company to offer each model in hundreds of colors. Instead, for each model
year a small number of color palettes is offered, each containing the colors for the body, trim, interior, etc. Another example
is the fashion industry, where in addition to solid colors there are also patterns, leading to a huge variety of combinations
that would be impossible to stock.
The traditional solution is that of "color forecasting." Color consultants assess the sentiment or affective state of a
target customer class and compare it with new colorants offered by the industry. They assemble a limited color palette,
name the colors according to the sentiment, and publish their result. Textile manufacturers will produce fabrics in these
colors and fashion designers will design clothes, accessories, and furniture based on these fabrics. Eventually, the media
will communicate these forecasts to the consumers, who will be admired by their cohorts when they choose colors from
the forecast palette, which by then is widely diffused.
The color forecasting business is very labor intensive and difficult, thus for years computer engineers have tried to come
up with algorithms to design harmonious color palettes, alas with little commercial success. For example, Johannes Itten's
color theory has been implemented many times, but despite Itten's success in the Bauhaus artifacts, the computer tools
have been of little utility. Indeed, contrary to the auditory sense, there is no known physiological mechanism sustaining
harmony and the term "harmonious" just has the informal meaning of "going well together."
We argue that the intellectual flaw resides in the belief that a masterful individual can devise a "perfect methodology"
that the engineer can then reduce to practice in a computer program. We suggest that the correct approach is to consider
color forecasting as an act of distillation, where a palette is digested from the sentiment of a very large number of people.
We describe how this approach can be reduced to an algorithm by replacing the subjective process with a data analytic
process.
