Data-Driven Color Manifolds

Color selection is required in many computer graphics applications, but can be tedious, as 1D or 2D user interfaces are employed to navigate in a 3D color space. Until now the problem was considered a question of designing general color spaces with meaningful (e.g., perceptual) parameters. In this work, we show how color selection usability improves by applying 1D or 2D color manifolds that predict the most likely change of color in a specific context. A typical use-case is manipulating the color of a banana; instead of presenting a 2D+1D RGB, CIE Lab, or HSV widget, our approach presents a simple 1D slider that captures the most likely change for this context. Technically, for each context, we learn a lower-dimensional manifold with varying density from labeled Internet examples. We demonstrate the increase in task performance of color selection in a user study.