Semantic Colorization with Internet Images

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Colorization of a grayscale photograph often requires considerable effort from the user, either by placing numerous color scribbles over the image to initialize a color propagation algorithm, or by looking for a suitable reference image from which color information can be transferred. Even with this user supplied data, colorized images may appear unnatural as a result of limited user skill or inaccurate transfer of colors. To address these problems, we propose a colorization system that leverages the rich image content on the internet. As input, the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene. With this information, images are downloaded from photo sharing websites and filtered to obtain suitable reference images that are reliable for color transfer to the given grayscale photo. Different image colorizations are generated from the various reference images, and a graphical user interface is provided to easily select the desired result. Our experiments and user study demonstrate the greater effectiveness of this system in comparison to previous techniques.
Publisher
ASSOC COMPUTING MACHINERY
Issue Date
2011-12
Language
English
Article Type
Article
Keywords

COLOR TRANSFER; GRAPH CUTS

Citation

ACM TRANSACTIONS ON GRAPHICS, v.30, no.6

ISSN
0730-0301
DOI
10.1145/2024156.2024190
URI
http://hdl.handle.net/10203/97737
Appears in Collection
EE-Journal Papers(저널논문)
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