Example-based painting guided by color features
In this paper, by analyzing and learning the color features of the reference painting with a novel set of measures, an example-based approach is developed to transfer some key color features from the template to the source image. First, color features of a given template painting is analyzed in terms of hue distribution and the overall color tone. These features are then extracted and learned by the algorithm through an optimization scheme. Next, to ensure the spatial coherence of the final result, a segmentation based post processing is performed. Finally, a new color blending model, which avoids the dependence of edge detection and adjustment of inconvenient tune parameters, is developed to provide a flexible control for the accuracy of painting. Experimental results show that the new example-based painting system can produce paintings with specific color features of the template, and it can also be applied to changing color themes of art pieces, designing color styles of paintings/real images, and specific color harmonization.
example-based painting image processing color features learning