Artistic Painting Style Transformation Using a Patch-based Sampling Method

Painting style transformation aims to produce a painting in a particular artist’s style from a picture or other paintings. In the previous implementations of example-based painting style transfer, users had to manually select the patches from example patches. It is time-consuming and subjective for a user to select proper patches from a large number of patches. This paper develops a patch-based approach for rendering example-based images without requiring user intervention to find appropriate patches in the synthesis process. We use mean shift segmentation and texture re-synthesis to construct an artistic database which enables users to synthesize images according to a selected painting style. Moreover, seven important painting features are proposed for finding an adequate correspondence between the source image and the database. The synthesized output images are generated by patch-based sampling method after the correspondence has been determined. Experimental results show the feasibility of the proposed approach by demonstrating the synthesis results from different types of source images with the painting style of Vincent van Gogh.