  Vector graphic documents present multiple visual elements, such as images,
shapes, and texts. Choosing appropriate colors for multiple visual elements is
a difficult but crucial task for both amateurs and professional designers.
Instead of creating a single color palette for all elements, we extract
multiple color palettes from each visual element in a graphic document, and
then combine them into a color sequence. We propose a masked color model for
color sequence completion and recommend the specified colors based on color
context in multi-palette with high probability. We train the model and build a
color recommendation system on a large-scale dataset of vector graphic
documents. The proposed color recommendation method outperformed other
state-of-the-art methods by both quantitative and qualitative evaluations on
color prediction and our color recommendation system received positive feedback
from professional designers in an interview study.
