Style-aware robust color transfer, H. Hristova, O. Le Meur, R. Cozot and K. Bouatouch, Computational Aesthetic, 2015. Transferring features, such as light and colors, between input and reference images is the main objective of color transfer methods. Current state-of-the-art methods focus mainly on the complete transfer of the light and color distributions. However, they do not successfully grasp specific light and color variations in image styles. In this paper, we propose a local method for carrying out a transfer of style between two images. Our method partitions both images to Gaussian distributed clusters by considering their main style features. These features are automatically determined by the classification step of our algorithm. Moreover, we present several novel policies for input/reference cluster mapping, which have not been tackled so far by previous methods. To complete the style transfer, for each pair of corresponding clusters, we apply a parametric color transfer method and a local chromatic adaptation transform. Results, subjective user evaluation as well as objective evaluation show that the proposed method obtains visually pleasing and artifact-free images, respecting the reference style.

1. 1/51
Introduction Our method Summary
Style-aware robust color transfer
H. Hristova O. Le Meur R. Cozot K. Bouatouch
IRISA - University of Rennes 1
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

2. 2/51
Introduction Our method Summary
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

3. 3/51
Introduction Our method Summary
Context
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

4. 4/51
Introduction Our method Summary
Context
Color transfer domain
Objective of color transfer
Color transfer aims at modifying the look of an input image
considering the illumination and the color palette of a reference
image (e.g., considering statistical characteristics of the light and
color distributions, histograms, gradients, etc.).
Input Our result Reference
Transformation
f(µ, Σ, φ, ...)
light, colors light, colors
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

5. 5/51
Introduction Our method Summary
Context
State-of-the-art global methods
[Reinhard et al., 2001]
Linear mapping
L

6. Diagonal covariance matrix
Gaussian distribution
[Pitie et al., 2007]
Linear mapping as a solution to Monge-Kantorovich
optimization problem
CIE Lab
Non-diagonal covariance matrix
Gaussian distribution
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

7. 6/51
Introduction Our method Summary
Context
State-of-the-art local methods
[Tai et al., 2005]
Linear mapping: [Reinhard et al., 2001]
L

8. 3D image clustering
Cluster mapping: maps the closest clusters in terms of their
luminance channel values
Gaussian distribution (cluster-wise)
[Bonneel et al., 2013]
Linear mapping: [Pitie et al., 2007]
CIE Lab
Image clustering in terms of the luminance channel
Cluster mapping: shadows to shadows, midtones to midtones,
highlights to highlights
Gaussian distribution (cluster-wise)
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

9. 7/51
Introduction Our method Summary
Context
Limitations
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

10. 7/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

11. 7/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
InputimageReferenceimage[Reinhardetal.2001]
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

12. 8/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

13. 8/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

14. 9/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Clustering on the same
color space components
for input/reference images
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

15. 9/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Clustering on the same
color space components
for input/reference images
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

16. 10/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Clustering on the same
color space components
for input/reference images
Over-saturation or
lack of contrast
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

17. 10/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Clustering on the same
color space components
for input/reference images
Over-saturation or
lack of contrast
[Pitie et al., 2007]
[Reinhard et al., 2001]
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

18. 11/51
Introduction Our method Summary
Context
Limitations
Style inconsistency
Gaussian distribution
Clustering on the same
color space components
for input/reference images
Over-saturation or
lack of contrast
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

19. 12/51
Introduction Our method Summary
Objective
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

20. 13/51
Introduction Our method Summary
Objective
Our objective
Transferring the light and color features of a reference image to an
input image with respect to the reference style features. Obtaining
a naturally looking image which style features are as close as
possible to those of the reference style.
Style notion in our context
In our method, style in images is characterized by two features:
color and light.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

21. 14/51
Introduction Our method Summary
Objective
Example
Input Reference
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

22. 15/51
Introduction Our method Summary
Objective
Example
Input Reference
[Pitieetal.,2007]
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

23. 16/51
Introduction Our method Summary
Objective
Example
Input Reference
[Pitieetal.,2007][Bonneeletal.,2013]
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

24. 17/51
Introduction Our method Summary
Objective
Example
Input Reference
[Pitieetal.,2007][Bonneeletal.,2013]
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

25. 18/51
Introduction Our method Summary
Contributions
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

26. 19/51
Introduction Our method Summary
Contributions
Classi

27. cation of the input and reference images
Light-based style images
Colors-based style images
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

28. 19/51
Introduction Our method Summary
Contributions
Classi

29. cation of the input and reference images
Light-based style images
Colors-based style images
Two ways of clustering:
Luminance channel
Luminance-Hue distributions
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

30. 19/51
Introduction Our method Summary
Contributions
Classi

31. cation of the input and reference images
Light-based style images
Colors-based style images
Two ways of clustering:
Luminance channel
Luminance-Hue distributions
Four mapping policies: Light to Light, Light to Colors, Colors
to Light, Colors to Colors
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

32. 19/51
Introduction Our method Summary
Contributions
Classi

33. cation of the input and reference images
Light-based style images
Colors-based style images
Two ways of clustering:
Luminance channel
Luminance-Hue distributions
Four mapping policies: Light to Light, Light to Colors, Colors
to Light, Colors to Colors
Evaluation of the results
Subjective user study
Objective metrics
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

34. 20/51
Introduction Our method Summary
Algorithm
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

35. 21/51
Introduction Our method Summary
Algorithm
Framework
light, colors
light, colors
Input
image
Reference
image
Classification
Clustering
Clustering
Chromatic
adaptation
Color
transfer
Mergingprocess
Mapping
policies
Input
clusters
Set of corresponding
clusters
Final
image
Reference
clusters
Classification
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

36. 22/51
Introduction Our method Summary
Algorithm
Automatic image classi

37. cation system
Colors-based style images
Images which color information is sucient enough to well-de

38. ne
at least two dierent and signi

39. cant colors.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

40. 23/51
Introduction Our method Summary
Algorithm
Automatic image classi

41. cation system
Light-based style images
Images which are not classi

42. ed as colors-based style images are
classi

43. ed as light-based style images. Their light features are more
meaningful than their color features.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

44. 24/51
Introduction Our method Summary
Algorithm
Framework
light, colors
light, colors
Input
image
Reference
image
Classification
Clustering
Clustering
Chromatic
adaptation
Color
transfer Mergingprocess
Mapping
policies
Input
clusters
Set of corresponding
clusters
Final
image
Reference
clusters
Classification
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

45. 25/51
Introduction Our method Summary
Algorithm
Clustering: Gaussian mixture models
Light-based style image Colors-based style image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

46. 26/51
Introduction Our method Summary
Algorithm
Framework
light, colors
light, colors
Input
image
Reference
image
Classification
Clustering
Clustering
Chromatic
adaptation
Color
transfer Mergingprocess
Mapping
policies
Input
clusters
Set of corresponding
clusters
Final
image
Reference
clusters
Classification
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

47. 27/51
Introduction Our method Summary
Algorithm
Pseudo code algorithm
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Darkest cluster - the cluster
which centroid has the minimum
luminance value
Coldest cluster - the cluster
which centroid has the
maximum hue value
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

48. 28/51
Introduction Our method Summary
Algorithm
Mapping policies
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Darkest cluster - the cluster
which centroid has the minimum
luminance value
Coldest cluster - the cluster
which centroid has the
maximum hue value
Light to Colors
Input light-based style
image
Reference colors-based
style image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

49. 29/51
Introduction Our method Summary
Algorithm
Mapping policies
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Darkest cluster - the cluster
which centroid has the minimum
luminance value
Coldest cluster - the cluster
which centroid has the
maximum hue value
Colors to Light
Input colors-based style
image
Reference light-based style
image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

50. 30/51
Introduction Our method Summary
Algorithm
Mapping policies
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Darkest cluster - the cluster
which centroid has the minimum
luminance value
Coldest cluster - the cluster
which centroid has the
maximum hue value
Light to Light
Input light-based style
image
Reference light-based style
image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

51. 31/51
Introduction Our method Summary
Algorithm
Mapping policies
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab = PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Darkest cluster - the cluster
which centroid has the minimum
luminance value
Coldest cluster - the cluster
which centroid has the
maximum hue value
Colors to Colors
Input colors-based style
image
Reference colors-based
style image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

52. 32/51
Introduction Our method Summary
Algorithm
Color transformation
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Color transformation between
each two corresponding clusters
Carried out on the a and b
channels of CIE Lab
Closed-form solution: solution to
Monge-Kantorovich's
optimization problem
Non-diagonal covariance matrix
between the channels of the
color space
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

53. 33/51
Introduction Our method Summary
Algorithm
Color transformation
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Color transformation between
each two corresponding clusters
Carried out on the a and b
channels of CIE Lab
Closed-form solution: solution to
Monge-Kantorovich optimization
problem
Non-diagonal covariance matrix
between the channels of the
color space
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

54. 34/51
Introduction Our method Summary
Algorithm
Chromatic adaptation
for k = 1; : : : ; N do
if Light to Colors then
ILab := FindDarkestCluster()
JLab := FindColdestCluster()
end if
if Colors to Light then
ILab := FindColdestCluster()
JLab := FindDarkestCluster()
end if
if Light to Light then
ILab := FindDarkestCluster()
JLab := FindDarkestCluster()
end if
if Colors to Colors then
[ILab, JLab] := FindMinDistPair()
end if
OLab := PerformTranspOnAB(ILab, JLab)
Exclude ILch
Exclude JLch
end for
Ofinal
rgb := CATLocal(Orgb; Jrgb)
Adapting pixel-wisely the colors
of the output to the reference
illuminant
Input illuminant: white image
obtained by performing Gaussian
low-pass

55. lter
Reference illuminant estimation:
Gray World assumption
[FSDP14, HCWxW06]
Local CAT algorithm [KJF07]
Avoids undesired color
saturation, prevents the result
from becoming
at and reduces
false colors in the result
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

56. 35/51
Introduction Our method Summary
Evaluation
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

57. 36/51
Introduction Our method Summary
Evaluation
Subjective evaluation
15 users
Results from 4
state-of-the-arts
Results from our
method
Style match
evaluation
Aesthetic
pleasingness
evaluation
5-point scale
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

58. 37/51
Introduction Our method Summary
Evaluation
Subjective evaluation: the analysis
Ours Pitie Reinhard Bonneel Tai
2345
Ours Pitie Reinhard Bonneel Tai
Stylescore
*
*
***
***
Ours Pitie Reinhard Bonneel Tai
12345
Ours Pitie Reinhard Bonneel TaiAestheticsscore
**
***
*
Box-and-Whisker plots
Paired T-tests
Correlation between the two scores
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

59. 38/51
Introduction Our method Summary
Evaluation
Objective evaluation
SSIM
It measures the degree of artifacts in the result. It is applied
between the input image and the result.
S(x; y) = f (c(x; y); s(x; y)) (1)
The luminance component is removed from the computation of the
metrics leaving the structural and contrast components.
Bhattacharya coecient
Measures the distance between the result/reference histograms
[ATR98] of the components of CIE Lab color space.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

60. 39/51
Introduction Our method Summary
Evaluation
Objective evaluation: the analysis
(a) Our method (b) Pitié et al. (c) Bonneel at al.(d) Reinhard et al. (e) Tai et al.
SSIM
Bhattacharya
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1
2
3
4
5
6
7
8
9
x 10
−3
SSIM
Bhattacharya
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1
2
3
4
5
6
7
x 10
−3
SSIM
Bhattacharya
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1
2
3
4
5
6
7
x 10
−3
SSIM
Bhattacharya
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1
2
3
4
5
6
7
x 10
−3
SSIM
Bhattacharya
0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
x 10
−3
Joint distribution of SSIM and Bhattacharya coecient
Paired T-tests
(SSIM, Bhattacharya) = (1, 1): optimal for the pair
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

61. 40/51
Introduction Our method Summary
Results
Outline
1 Introduction
Context
Objective
Contributions
2 Our method
Algorithm
Evaluation
Results
3 Summary
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

62. 41/51
Introduction Our method Summary
Results
Results: Light to Colors
Result
Input image
Reference image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

63. 42/51
Introduction Our method Summary
Results
Results: Colors to Light
Input image
Reference image Result
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

64. 43/51
Introduction Our method Summary
Results
Results: Light to Light
Result
Input image
Reference image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

65. 44/51
Introduction Our method Summary
Results
Results: Colors to Colors
Result
Input image
Reference image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

66. 45/51
Introduction Our method Summary
Results
Results: classical example
Result
Input image
Reference image
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

67. 46/51
Introduction Our method Summary
Summary
New method for style transfer for a wide class of image pairs
Automatic image classi

68. cation
Four mappings policies
Subjective and objective evaluations
Future work
Enriching the image feature space
Finding a connection between the subjective user study and
the objective evaluation
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

69. 47/51
Introduction Our method Summary
Thank you for your attention!
More results and information on
http: // people. irisa. fr/ Hristina. Hristova
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

70. 48/51
Appendix
References
References I
Aherne F. J., Thacker N. A., Rockett P. I.:
The bhattacharyya metric as an absolute similarity measure for
frequency coded data.
Kybernetika 34, 4 (1998), 363{368.
Bonneel N., Sunkavalli K., Paris S., Pfister H.:
Example-based video color grading.
ACM Transactions on Graphics (Proceedings of SIGGRAPH
2013) 32, 4 (2013), 2.
Frigo O., Sabater N., Demoulin V., Pierre H.:
Optimal transportation for example-guided color transfer.
12th Asian Conference on Computer Vision (ACCV) (2014).
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

71. 49/51
Appendix
References
References II
Huo J., Chang Y., Wang J., xia Wei X.:
Robust automatic white balance algorithm using gray color
points in images.
IEEE Trans. Consumer Electronics 52, 2 (2006), 541{546.
URL: http://dblp.uni-trier.de/db/journals/tce/
tce52.html#HuoCWW06.
Kuang J., Johnson G. M., Fairchild M. D.:
icam06: A re

72. ned image appearance model for hdr image
rendering.
Journal of Visual Communication and Image Representation
18, 5 (2007), 406{414.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

73. 50/51
Appendix
References
References III
Pitie F., Kokaram A. C., Dahyot R.:
Automated colour grading using colour distribution transfer.
Computer Vision and Image Understanding 107, 1 (2007),
123{137.
Reinhard E., Ashikhmin M., Gooch B., Shirley P.:
Color transfer between images.
Computer Graphics and Applications, IEEE 21, 5 (2001),
34{41.
Shih Y., Paris S., Durand F., Freeman W. T.:
Data-driven hallucination of dierent times of day from a
single outdoor photo.
ACM Transactions on Graphics (TOG) 32, 6 (2013), 200.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch

74. 51/51
Appendix
References
References IV
Tai Y.-W., Jia J., Tang C.-K.:
Local color transfer via probabilistic segmentation by
expectation-maximization.
In Computer Vision and Pattern Recognition, 2005. CVPR
2005. IEEE Computer Society Conference on (2005), vol. 1,
IEEE, pp. 747{754.
Style-aware robust color transfer H. Hristova, O. Le Meur, R. Cozot, K. Bouatouch