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

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

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

Template by COLOURLover Any Palacios

Coloring Patterns Can Be Challenging

Difficult to explore other options

Such as:

Output Suggested Colorings

Suggest Pattern Colorizations to Facilitate the Process

?

User preferences

Input Template

Output Suggested Colorings

Suggest Pattern Colorizations to Facilitate the Process

?

User preferences

Input Template

Suggest diverse colorings

Allow refinement

Accommodate stylistic

preferences

Pattern Template AnatomyColor GroupsColored Template

COLOURLovers Nickity Split & ivy21

Related Work: Color Compatibility

What combinations of colors do people find appealing?

(Goethe 1810; Itten 1974; Matsuda 1995; Cohen-Or et Al 2006)

Related Work: Color Compatibility

What combinations of colors do people find appealing?

Low compatibility

High compatibility

(O’Donovan et al. 2011)

Color Compatibility for Patterns

Need to take into account 2D arrangement

3.75 3.74 3.70 3.67

Template by COLOURLover jilbert

“loud” backgroundleaves blending into background

What about personal preferences?

Look at Examples for Guidance

COLOURLovers AlineDam, Any Palacios, wondercake, bhsav

Example-Based Color Suggestion

ModelSuggest

er

(optional) user constraints

Input Template

Output Suggested Colorings

Examples

COLOURLovers AlineDam, Any Palacios, wondercake, bhsav

Can Change Style Based on Examples

ModelSuggest

er

(optional) user constraints

Input Template

Output Suggested Colorings

Examples

COLOURLovers AlineDam, Any Palacios, praxicalidocious, bhsav

Dataset: COLOURLoversMany patterns

available:Collected 8200 from 82 artists

For our tests: Trained on up to 913 patterns

MODEL

Scoring a Coloring

Unary Factors

Scoring a Coloring

Good

Scoring a Coloring

Poor

?

?

?

Scoring a Coloring

Scoring a Coloring

Scoring a Coloring

Scoring a Coloring

Pairwise

Factors

Scoring a Coloring

Scoring a Coloring

Color Theme:

Global Color Compatibilit

y[O’Donovan et al. 2011]

Scoring a Coloring

Color Theme:

Global Color Compatibilit

y[O’Donovan et al. 2011]

Scoring a Coloring

Color Theme:

Scoring a Coloring

Modeling Unary Color Factors

Property of

Region’s Color

Features of

Region’s Shape

Lightness

Saturation

Name Saliency [Heer & Stone

2012]

Modeling Unary Color Factors

Property of

Region’s Color

Features of

Region’s Shape

…Size

Elongation

Centrality

Learning Factor Distributions

Predictor

0.2

0.7

0.4

=Size

Elongation

Centrality …

0.8

0.92

LearnerSaturation

Distribution=

Learning Factor Distributions

0 1Lightness

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

0.4

0.1

0.5

0.10.2

0.1

0.4

0.20.1

0.1

0.9

0.7

Lightness

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

0.4

0.1

0.5

0.10.2

0.1

0.4

0.20.1

0.1

0.9

0.7

Lightness

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

1

Lightness

0.4

0.1

0.5

0.2

0.1

0.4

0.20.1

0.1

0.9

0.7

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

1

Lightness

0.4

0.1

0.5

0.2

0.1

0.4

0.20.1

0.1

0.9

0.7

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

2

Lightness

…1

0.4

0.1

0.5

0.2

0.1

0.4

0.1

0.1

0.9

0.7

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

Lightness

…0.1

0.1

0.9

0.7210.4

0.1

0.5

0.2

0.1

0.4

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

7

Lightness

0.1

0.1

0.9

210.4

0.1

0.5

0.2

0.1

0.4

Learning Factor Distributions

0 91 2 3 4 5 6 7 8

Classifier0.2

0.7

0.4

?

0

1

Lightness

7

…0.1

0.1

0.9

210.4

0.1

0.5

0.2

0.1

0.4

Learning Factor Distributions

0

1

0 1Lightness

Classifier0.2

0.7

0.4

?

70.1

0.1

0.9

210.4

0.1

0.5

0.2

0.1

0.4

Learning Factor Distributions

0

1

10 1Lightness

Classifier0.2

0.7

0.4

?

70.1

0.1

0.9

210.4

0.1

0.5

0.2

0.1

0.4

[Charpiat et al. 2008]

Example Learned Factors

Scoring a Coloring (Revisited)

Unary Factors

Pairwise

Factors

Global Color Compatibilit

y[O’Donovan et al. 2011]

Scoring a Coloring (Revisited)

Score = Product of Factors

(Factor Graph)

Generating Coloring SuggestionsMetropolis Hastings (MH)

Parallel Tempering

Maximum Marginal Relevance

REJECT

ACCEPT ACCEPT

RESULTS

Exploratory Suggestions

Refinement: Nearby Colorings

Refinement: Hard Constraints

Unconstrained

Flower Stem Color =

Style SimulationLight

Dark

Bold

Mellow

Application: Web Design

Application: Fashion Design

EVALUATION

4 x Uniform Random

4 x Color Compatibility

Only

4 x Full Model

4 x Hand-Colored

Better Than Other Automatic Methods(but not hand-colored patterns)

People Make ‘Bad’ Colorings Just as Often

FUTURE WORK

Limitation: Semantics

“sky”

Limitation: Known Color Groups

1 2 3 4 5

? ?

Integration into Interactive Tools

Looking Forward

THANKS!Support for this research provided

by:

Intel (ISTC-VC)

SAP (Stanford Graduate

Fellowship)