Color Orchestra: Ordering Color Palettes for Interpolation and Prediction

Color theme or color palette can deeply influence the quality and the feeling of a photograph or a graphical design. Although color palettes may come from different sources such as online crowd-sourcing, photographs and graphical designs, in this paper, we consider color palettes extracted from fine...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2018-06, Vol.24 (6), p.1942-1955
Main Authors: Phan, Huy Q., Hongbo Fu, Chan, Antoni B.
Format: Article
Language:English
Subjects:
Artists
Classification
Color
color palette
colorization
Density
Digitization
Estimation
Image color analysis
Interpolation
machine learning
Real-time systems
Software reviews
Sorting algorithms
Statistical models
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Summary:Color theme or color palette can deeply influence the quality and the feeling of a photograph or a graphical design. Although color palettes may come from different sources such as online crowd-sourcing, photographs and graphical designs, in this paper, we consider color palettes extracted from fine art collections, which we believe to be an abundant source of stylistic and unique color themes. We aim to capture color styles embedded in these collections by means of statistical models and to build practical applications upon these models. As artists often use their personal color themes in their paintings, making these palettes appear frequently in the dataset, we employed density estimation to capture the characteristics of palette data. Via density estimation, we carried out various predictions and interpolations on palettes, which led to promising applications such as photo-style exploration, real-time color suggestion, and enriched photo recolorization. It was, however, challenging to apply density estimation to palette data as palettes often come as unordered sets of colors, which make it difficult to use conventional metrics on them. To this end, we developed a divide-and-conquer sorting algorithm to rearrange the colors in the palettes in a coherent order, which allows meaningful interpolation between color palettes. To confirm the performance of our model, we also conducted quantitative experiments on datasets of digitized paintings collected from the Internet and received favorable results.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2017.2697948