Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs

Colored pattern images appear everywhere; they are used as background and header images in web design, as decorations in games, in clothing and upholstery, and in personal arts and crafts. Choosing colors for a pattern is one way to personalize and enliven the design composition. However, it can be difficult for a beginning artist or enthusiast to effectively explore the large space of possible colorings. In this talk, we look at automatically generating coloring suggestions to help the user through the creative coloring process.

We present a probabilistic factor graph model for automatically coloring patterns. The model is trained on example patterns to statistically capture their stylistic properties. It incorporates terms for enforcing both color compatibility and spatial arrangements of colors that are consistent with the training examples. Using Markov Chain Monte Carlo, the model can be sampled to generate a diverse set of new colorings for a target pattern. This general probabilistic framework also allows users to guide the generated suggestions via conditional inference or additional soft constraints. We demonstrate results on a variety of coloring tasks, and show a demo vector-art-editing application that incorporates the suggestion engine. Similar techniques may be useful for other domains of graphic art, such as typography and layout.

Panel: Sharon Lin, Ph.D. candidate, Stanford University
Daniel Ritchie, Ph.D. candidate, Stanford University
Matthew Fisher, Ph.D & PostDoc, Stanford University