Abstract We propose a novel method that automatically analyzes stroke-related artistic styles of paintings. A set of adaptive interfaces are also developed to connect the style analysis with existing painterly rendering systems, so that the specific artistic style of a template painting can be effectively transferred to the input photo with minimal effort. Different from conventional texture-synthesis based rendering techniques that focus mainly on texture features, this work extracts, analyzes and simulates high-level style features expressed by artists ’ brush stroke techniques. Through experiments, user studies and comparisons with ground truth, we demonstrate that the proposed style-orientated painting framework can significantly reduce tedious parameter adjustment, and it allows amateur users to efficiently create desired artistic styles simply by specifying a template painting.

Most of the parameters such as some thresholds are tuned through many experimental tests and can work well for all the experiments. The only parameters we have to tune are the mean shift parameters which can control the granularity of segmentation as shown in Fig. 1 of our manuscript. Besides, we have experimented the “Road ” example with the most important parameters wc, wd and ws changed to 0.9 respectively in the iterative optimization stage, as shown in Fig. 1. Since wc, wd and ws control the Gaussian models of color, disparity and spatial distribution respectively without temporal statistics, they should not be set too large. Otherwise, the generated segmentation results cannot preserve consistent boundaries well, and will contain many segmentation noises, as shown in Fig. 1(b-d).

Artistic preprocessing for painterly rendering and image stylization