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

Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs. Sharon Lin, Daniel Ritchie, Matthew Fisher, Pat Hanrahan. Colored Patterns Are Everywhere. Flickr : Rowena of the Rants. Coloring Patterns Can Be Challenging. Hard to mentally visualize coloring.

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

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  1. Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs Sharon Lin, Daniel Ritchie, Matthew Fisher, Pat Hanrahan

  2. Colored Patterns Are Everywhere Flickr: Rowena of the Rants

  3. Coloring Patterns Can Be Challenging • Hard to mentally visualize coloring Template by COLOURLover Any Palacios

  4. Coloring Patterns Can Be Challenging • Difficult to explore other options Such as:

  5. Suggest Pattern Colorizations to Facilitate the Process User preferences ? Input Template Output Suggested Colorings

  6. Suggest Pattern Colorizations to Facilitate the Process User preferences • Suggest diverse colorings • Allow refinement • Accommodate stylistic preferences ? Input Template Output Suggested Colorings

  7. Pattern Template Anatomy Color Groups Colored Template COLOURLovers Nickity Split & ivy21

  8. Related Work: Color Compatibility • What combinations of colors do people find appealing? (Goethe 1810; Itten 1974; Matsuda 1995; Cohen-Or et Al 2006)

  9. Related Work: Color Compatibility • What combinations of colors do people find appealing? Low compatibility High compatibility (O’Donovan et al. 2011)

  10. Color Compatibility for Patterns 3.74 3.70 3.75 3.67 “loud” background leaves blending into background What about personal preferences? Need to take into account 2D arrangement Template by COLOURLoverjilbert

  11. Look at Examples for Guidance COLOURLoversAlineDam, Any Palacios, wondercake, bhsav

  12. Example-Based Color Suggestion Examples (optional) user constraints … Model Suggester Input Template Output Suggested Colorings COLOURLoversAlineDam, Any Palacios, wondercake, bhsav

  13. Can Change Style Based on Examples Examples (optional) user constraints … Model Suggester Input Template Output Suggested Colorings COLOURLoversAlineDam, Any Palacios, praxicalidocious, bhsav

  14. Dataset: COLOURLovers • Many patterns available: • Collected 8200 from 82 artists • For our tests: • Trained on up to 913 patterns

  15. model

  16. Scoring a Coloring Unary Factors

  17. Scoring a Coloring Good

  18. Scoring a Coloring ? ? Poor ?

  19. Scoring a Coloring

  20. Scoring a Coloring

  21. Scoring a Coloring

  22. Scoring a Coloring Pairwise Factors

  23. Scoring a Coloring

  24. Scoring a Coloring Global Color Compatibility [O’Donovan et al. 2011] Color Theme:

  25. Scoring a Coloring Global Color Compatibility [O’Donovan et al. 2011] Color Theme:

  26. Scoring a Coloring Color Theme:

  27. Scoring a Coloring

  28. Modeling Unary Color Factors Property of Region’s Color Features of Region’s Shape • Lightness • Saturation • Name Saliency [Heer & Stone 2012] …

  29. Modeling Unary Color Factors Property of Region’s Color Features of Region’s Shape • Size • Elongation • Centrality …

  30. Learning Factor Distributions Learner Saturation Size = Predictor Elongation Centrality 0.8 … 0.92 Distribution =

  31. Learning Factor Distributions 0 1 Lightness

  32. Learning Factor Distributions 0.2 0.7 0.1 … Lightness 0 1 2 3 4 5 7 8 9 6

  33. Learning Factor Distributions 0.2 0.7 0.1 … Lightness 0 1 2 3 4 5 7 8 9 6

  34. Learning Factor Distributions 1 0.2 0.7 … Lightness 0 1 2 3 4 5 7 8 9 6

  35. Learning Factor Distributions 1 0.2 0.7 … Lightness 0 1 2 3 4 5 7 8 9 6

  36. Learning Factor Distributions 1 2 0.7 … Lightness 0 1 2 3 4 5 7 8 9 6

  37. Learning Factor Distributions 1 2 0.7 … Lightness 0 1 2 3 4 5 7 8 9 6

  38. Learning Factor Distributions 7 1 2 … Lightness 0 1 2 3 4 5 7 8 9 6

  39. Learning Factor Distributions 7 1 2 … ? Classifier 1 Lightness 0 1 2 3 4 5 7 8 9 6 0

  40. Learning Factor Distributions 7 1 2 … ? Classifier 1 0 1 0 Lightness

  41. Learning Factor Distributions 7 1 2 … ? Classifier 1 0 1 1 0 Lightness [Charpiat et al. 2008]

  42. Example Learned Factors

  43. Scoring a Coloring (Revisited) Pairwise Factors Unary Factors Global Color Compatibility [O’Donovan et al. 2011]

  44. Scoring a Coloring (Revisited) Score = Product of Factors (Factor Graph)

  45. Generating Coloring Suggestions • Metropolis Hastings (MH) • Parallel Tempering • Maximum Marginal Relevance ACCEPT ACCEPT REJECT

  46. RESULTS

  47. Exploratory Suggestions

  48. Refinement: Nearby Colorings

  49. Refinement: Hard Constraints Unconstrained Flower Stem Color =

  50. Style Simulation Light Dark Bold Mellow

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