color compatibility from large datasets peter o’donovan university of toronto aseem agarwala adobe...
TRANSCRIPT
![Page 1: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/1.jpg)
Color Compatibility From Large Datasets
Peter O’DonovanUniversity of Toronto
Aseem AgarwalaAdobe Systems, Inc.
Aaron HertzmannUniversity of Toronto
![Page 2: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/2.jpg)
Choosing colors is hard for many people
![Page 3: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/3.jpg)
Choosing colors is hard for many people
![Page 4: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/4.jpg)
?
![Page 5: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/5.jpg)
How do designers choose colors?
![Page 6: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/6.jpg)
Picasso
How do designers choose colors?
![Page 7: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/7.jpg)
You the Designer
How do designers choose colors?
![Page 8: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/8.jpg)
Krause [2002]
How do designers choose colors?
![Page 9: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/9.jpg)
Goethe [1810]
Complementary Color Theory: colors opposite on the color wheel are compatible
![Page 10: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/10.jpg)
Hue Templates: relative orientations producing compatible colors
Complementary Monochromatic Analogous Triad
![Page 11: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/11.jpg)
Photo and Video Quality Evaluation:Focusing on the SubjectLuo and Tang 2008
Aesthetic Visual Quality Assessment of PaintingsLi and Chen 2009
Color Harmonization for VideosSawant and Mitra 2008
Color Harmonization Cohen-Or et al. 2006
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Adobe Kuler
527,935 themes
Ratings: 1-5 stars
![Page 14: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/14.jpg)
Adobe Kuler
527,935 themes
Ratings: 1-5 stars
![Page 15: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/15.jpg)
Adobe Kuler
527,935 themes
Ratings: 1-5 stars
![Page 16: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/16.jpg)
Adobe Kuler
527,935 themes
Ratings: 1-5 stars
![Page 17: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/17.jpg)
COLOURLovers
1,672,657 themes
Views and “Likes”
![Page 18: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/18.jpg)
COLOURLovers
1,672,657 themes
Views and “Likes”
![Page 19: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/19.jpg)
Goals
1. AnalysisTest hypotheses and compatibility models
2. Learn ModelsPredict mean ratings for themes
3. New ApplicationsDevelop new tools for choosing colors
![Page 20: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/20.jpg)
Goals
1. AnalysisTest hypotheses and compatibility models
2. Learn ModelsPredict mean ratings for themes
3. New ApplicationsDevelop new tools for choosing colors
![Page 21: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/21.jpg)
104,426 themes Ratings: 1-5 stars
383,938 themes # Views and “Likes”
Kuler Dataset COLOURLovers Dataset
![Page 22: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/22.jpg)
Mechanical Turk dataset
10,743 themes from Kuler40 ratings per theme1,301 total participants
![Page 23: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/23.jpg)
Overall preference for warmer hues and cyans
Histogram of hue usage
Hue
% of all themes
![Page 24: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/24.jpg)
Mean rating for themes containing a hue
Overall preference for warmer hues and cyansHue
Mean Rating
![Page 25: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/25.jpg)
Histogram of hue adjacency (Kuler)
![Page 26: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/26.jpg)
Histogram of hue adjacency (Kuler)
![Page 27: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/27.jpg)
is more likely than
Histogram of hue adjacency (Kuler)
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Significant structure
Histogram of hue adjacency (Kuler)
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Significant structureWarm hues pair well with each other
Histogram of hue adjacency (Kuler)
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Significant structureWarm hues pair well with each otherGreens and purples more compatible with themselves
Histogram of hue adjacency (Kuler)
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Hue Template Analysis
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Hue Templates: relative orientations producing compatible colors
![Page 33: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/33.jpg)
Templates are rotationally invariant
Hue Templates: relative orientations producing compatible colors
![Page 34: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/34.jpg)
Different templates equally compatible
Complementary Monochromatic Analogous Triad
Hue Templates: relative orientations producing compatible colors
![Page 35: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/35.jpg)
Diagonal lines are hue templates (Kuler interface bias)
Hue adjacency in a theme (Kuler)
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Complementary template
Hue adjacency in a theme (Kuler)
Diagonal lines are hue templates (Kuler interface bias)
![Page 37: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/37.jpg)
Hue adjacency in a theme (Kuler)
Complementary:
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Complementary: Data:
Hue adjacency in a theme (Kuler)
![Page 39: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/39.jpg)
In template theory, diagonals should be uniform
Hue adjacency in a theme (Kuler)
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In template theory, diagonals should be uniformLarge dark bands indicates no rotational invariance
Hue adjacency in a theme (Kuler)
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Kuler CL
Hue adjacency in a theme
COLOURLovers’ has less interface biasTemplates are not present
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Distance to template
Rating
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Distance to template
Themes near a template score worse
Rating
![Page 44: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/44.jpg)
Themes near a template score worse - “Newbie” factor - “Too simple” factor
Distance to template
Rating
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MTurk has no interface bias: much flatter
Distance to template
Rating
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Template Conclusions
1) Templates do not model color preferences2) Themes near a template do not score better
than those farther away3) Not all templates are equally popular
- Simple templates preferred (see paper)
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Hue Entropy: entropy of hues along the hue wheel
![Page 48: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/48.jpg)
Hue Entropy: entropy of hues along the hue wheel
Low Entropy
Few Distinct Colors
![Page 49: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/49.jpg)
Hue Entropy: entropy of hues along the hue wheel
Low Entropy High Entropy
Few Distinct Colors Many Distinct Colors
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Hue Entropy: entropy of hues along the hue wheel
Hue Entropy
Rating
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Hue Entropy: entropy of hues along the hue wheel
Hue Entropy
Rating
![Page 52: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/52.jpg)
Hue Entropy
Rating
Hue Entropy: entropy of hues along the hue wheel
![Page 53: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/53.jpg)
Hue Entropy: entropy of hues along the hue wheel
Hue Entropy
Rating
![Page 54: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/54.jpg)
Main Analysis Results
1. Overall preference for warmer hues and cyans
![Page 55: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/55.jpg)
Main Analysis Results
1. Overall preference for warmer hues and cyans
2. Strong preferences for certain adjacent colors
![Page 56: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/56.jpg)
Main Analysis Results
1. Overall preference for warmer hues and cyans
2. Strong preferences for certain adjacent colors
3. Hue templates a poor model for compatibility
![Page 57: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/57.jpg)
Main Analysis Results
1. Overall preference for warmer hues and cyans
2. Strong preferences for certain adjacent colors
3. Hue templates a poor model for compatibility
4. People prefer simpler themes (but not too simple)
![Page 58: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/58.jpg)
Main Analysis Results
1. Overall preference for warmer hues and cyans
2. Strong preferences for certain adjacent colors
3. Hue templates a poor model for compatibility
4. People prefer simpler themes (but not too simple)
See paper for other tests
![Page 59: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/59.jpg)
Goals
1. AnalysisTest hypotheses and compatibility models
2. Learn ModelsPredict mean ratings for themes
3. New ApplicationsDevelop new tools for choosing colors
![Page 60: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/60.jpg)
3.63
![Page 61: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/61.jpg)
3.63
𝑓 (𝒙 )=𝑦
![Page 62: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/62.jpg)
Mean rating over all users
3.63
𝑓 (𝒙 )=𝑦
![Page 63: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/63.jpg)
𝑓 (𝒙 )=𝑦
Features (326 total)- Colors, sorted colors, differences, min/max,
max-in, mean/std dev, PCA features, hue probability, hue entropy
- RGB, HSV, CIELab, Kuler color wheel- “Kitchen Sink”
3.63
![Page 64: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/64.jpg)
𝑓 (𝒙 )=𝑦
Models- Constant baseline: mean of training targets - SVM-R, KNN- Lasso
- Linear regression model with L1 norm on weights- Solutions have many zero weights: feature
selection
3.63
![Page 65: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/65.jpg)
Dataset MAE
Constant Baseline
KNN SVM-R
Lasso Lasso over Baseline
Kuler 0.572 0.533 0.531 0.521 9%
COLORLovers
0.703 0.674 0.650 0.644 8%
MTurk 0.267 0.205 0.182 0.179 33%
![Page 66: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/66.jpg)
Dataset MAE
Constant Baseline
KNN SVM-R
Lasso Lasso over Baseline
Kuler 0.572 0.533 0.531 0.521 9%
COLORLovers
0.703 0.674 0.650 0.644 8%
MTurk 0.267 0.205 0.182 0.179 33%
![Page 67: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/67.jpg)
Dataset MAE
Constant Baseline
KNN SVM-R
Lasso Lasso over Baseline
Kuler 0.572 0.533 0.531 0.521 9%
COLORLovers
0.703 0.674 0.650 0.644 8%
MTurk 0.267 0.205 0.182 0.179 33%
Many more ratings per theme in MTurk
![Page 68: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/68.jpg)
Dataset MAE
Constant Baseline
KNN SVM-R
Lasso Lasso over Baseline
Kuler 0.572 0.533 0.531 0.521 9%
COLORLovers
0.703 0.674 0.650 0.644 8%
MTurk 0.267 0.205 0.182 0.179 33%
MTurk has an average std dev of 0.33Kuler has an average std dev of 0.72
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MTurk Test Set
Human Rating
Lasso Rating
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High-rated
𝑦=3.90 , f (𝐱 )=3.41𝑦=3.79 , f (𝐱 )=3.50
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High-rated
Low-rated
𝑦=3.90 , f (𝐱 )=3.41𝑦=3.79 , f (𝐱 )=3.50
𝑦=1.71 , f ( 𝐱 )=2.27𝑦=1.78 , f (𝐱 )=2.25
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High-rated
Low-rated
High prediction error
𝑦=3.90 , f (𝐱 )=3.41𝑦=3.79 , f (𝐱 )=3.50
𝑦=1.71 , f ( 𝐱 )=2.27𝑦=1.78 , f (𝐱 )=2.25
𝑦=2.74 , f ( 𝐱 )=1.78𝑦=2.22 , f ( 𝐱 )=3.16
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Model Analysis
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Important Lasso Features
Positive: high lightness mean & max, mean hue probability
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Important Lasso Features
Positive: high lightness mean & max, mean hue probability
Negative: high lightness variance, min hue probability
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Goals
1. AnalysisTest hypotheses and compatibility models
2. Learn ModelsPredict mean ratings for theme
3. New ApplicationsDevelop new tools for choosing colors
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1. Improve a Theme
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Maximize regression score
Stay within a distance of original (L2 in CIELab)
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Select order which maximizes score
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Optimize colors with CMA [Hansen 1995]
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Original Best Order Color and Order
f (𝐱 )=2.92 f (𝐱 )=3.04 f (𝐱 )=3.35
f (𝐱 )=3.00 f (𝐱 )=3.11 f (𝐱 )=3.37
f (𝐱 )=3.50 f (𝐱 )=3.50 f (𝐱 )=3.70
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Original Best Order Color and Order
f (𝐱 )=2.92, 𝑦=3.04f (𝐱 )=3.04 , 𝑦=2.99f (𝐱 )=3.35 , 𝑦=3.40
f (𝐱 )=3.00 , 𝑦=2.96f (𝐱 )=3.11 , 𝑦=3.21
f (𝐱 )=3.50 , 𝑦=3.72f (𝐱 )=3.50 , 𝑦=3.69f (𝐱 )=3.70 , 𝑦=3.82
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MTurk A/B test with original and optimized themes
Order and Color
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2. Choose 5 colors that best ‘represent’ an image
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One approach: k-means clustering
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One approach: k-means clustering
This ignores color compatibility
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Optimize 5 colors that1) Match the image well2) Maximize regression
score
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Optimize 5 colors that1) Match the image well2) Maximize regression
scoreSee paper for details
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With Compatibility Model
W/O Compatibility Model
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MTurk A/B testwith and withoutcompatibility model
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3. Given 4 colors for foreground, suggest background
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Given 4 colors, choose 5th color to maximize score
Want contrast with existing colors
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Find next best color, away from previous choices
, , …
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Model Suggestions
Random Suggestions
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MTurk tests selecting ‘Worst’ and ‘Best’
4 model & 4 random
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Model Limitations & Future Work
![Page 97: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/97.jpg)
Hard to interpret
Features
Weights
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Model has very few abstract colors, only 1-D spatial layout
![Page 99: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/99.jpg)
VS
Model does not understand how colors are used
![Page 100: Color Compatibility From Large Datasets Peter O’Donovan University of Toronto Aseem Agarwala Adobe Systems, Inc. Aaron Hertzmann University of Toronto](https://reader035.vdocument.in/reader035/viewer/2022062515/56649ceb5503460f949b7b09/html5/thumbnails/100.jpg)
VS
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Conclusions
Color preferences are subjective, but analysis reveals many overall trends
Simple linear models can represent compatibility fairly well
Models can be useful for color selection tasks
Our datasets and learned models are available online
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