Learning Natural Colors for Image Recoloring

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dc.contributor.authorHuang, Hao-Zhien_US
dc.contributor.authorZhang, Song-Haien_US
dc.contributor.authorMartin, Ralph R.en_US
dc.contributor.authorHu, Shi-Minen_US
dc.contributor.editorJ. Keyser, Y. J. Kim, and P. Wonkaen_US
dc.date.accessioned2015-03-03T12:54:08Z
dc.date.available2015-03-03T12:54:08Z
dc.date.issued2014en_US
dc.description.abstractWe present a data-driven method for automatically recoloring a photo to enhance its appearance or change a viewer's emotional response to it. A compact representation called a RegionNet summarizes color and geometric features of image regions, and geometric relationships between them. Correlations between color property distributions and geometric features of regions are learned from a database of well-colored photos. A probabilistic factor graph model is used to summarize distributions of color properties and generate an overall probability distribution for color suggestions. Given a new input image, we can generate multiple recolored results which unlike previous automatic results, are both natural and artistic, and compatible with their spatial arrangements.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.doi10.1111/cgf.12498en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12498en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleLearning Natural Colors for Image Recoloringen_US
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