A probabilistic framework for component-based vector graphics

We propose a framework for data-driven manipulation and synthesis of component-based vector graphics. Using labelled vector graphical images of a given type of object as input, our processing pipeline produces training data, learns a probabilistic Bayesian network from that training data, and offer various data-driven vector-related tools using synthesis functions. The tools ranges from data-driven vector design to automatic synthesis of vector graphics. Our tools were well received by designers, our model provides good generalisation performance, also from small data sets, and our method for synthesis produces vector graphics deemed significantly more plausible compared with alternative methods.