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We present an automatic framework to extract color palettes from words. This is a novel approach in comparison to existing solutions, e.g. manual creation or extraction from images. The associations between words and colors are deduced from a large database of 6 million tagged images using a scalable data-mining technique. The palette creation can be constrained by the user to achieve a desired hue template. We first focus on single words and then extend to entire texts. We compare our results against Adobe Kuler, a widely used online platform of manually created color palettes. We show that our approach performs slightly better than its non-automatic counterpart in terms of user’s preference rankings. This is a good result because our method is fully automatic whereas Kuler relies on users’ palettes that are manually created and annotated.
Nathan Quentin Faivre, Inaki Asier Iturrate Gil, Michael Eric Anthony Pereira, Shuo Wang, Xiao Hu, Caroline Peters