Automatic image vectorization using superpixels and random walkers

Abstract

Image vectorization involves two major problems: how to extract proper geometric descriptors from the raster image and how to rasterize the vector representation for display. In this paper, we propose a novel image vectorization approach using diffusion curves as the geometric primitives. Our approach automatically extracts accurate diffusion curves from the input image without user interaction. We first segment the input image into a set of superpixels by a multi-layer algorithm. Then, boundary positions of these superpixels are explored to locate control points for diffusion curves, and color information is properly sampled to generate our double-boundary representation. To render the vector graphics, we formulate color diffusion as a random walk process. Experiments on different categories of photographs show that our approach successfully reveals detail contents in the reconstructed image, and that the rendering process can be performed nearly in realtime on a modern CPU.