Seed growing for interactive image segmentation with geodesic voting

In this paper, we propose a novel seed growing framework for interactive image segmentation. We first formulate the seed dependency problem in interactive segmentation and overcome it by expanding the seed automatically. To expand the user-input seed, we generate the seed distance maps based on color distribution dissimilarity, locational prior, and geodesic distance. Using these seed distance maps, we expand the seed by classifying the image into a trimap with unanimous voting. We then extract the skeleton from the foreground and background regions. Experiments show that the proposed framework provides significant support for existing interactive segmentation techniques.