Image Matting for Sparse User Input by Iterative Refinement

Abstract

Image matting is the process of extracting the foreground component from an image. Since matting is an under constrained problem most techniques address the case where users supply some dense labelling to indicate known foreground and background regions. In contrast to other techniques our proposed technique is unique in that focuses on achieving satisfactory results with extremely sparse input, e.g. a handful of individual pixel labels. We propose an iterative extension to the class of affinity matting techniques. Analysis of results from affinity matting with sparse labels reveals that the low quality alpha mattes can be processed and re-used for the next iteration. We demonstrate this extension using the recent KNN matting and show that this technique can greatly improve matting results.