Review of GrabCut in Image Processing

Abstract

As an image-segmentation method based on graph theory, GrabCut has attracted more and more researchers to pay attention to this new method because of its advantages of simple operation and excellent segmentation. In order to clarify the research status of GrabCut, we begin with the original GrabCut model, review the improved algorithms that are new or important based on GrabCut in recent years, and classify them in terms of pre-processing based on superpixel, saliency map, energy function modification, non-interactive improvement and some other improved algorithms. The application status of GrabCut in various fields is also reviewed. We also experiment with some classical improved algorithms, including GrabCut, LazySnapping, OneCut, Saliency Cuts, DenseCut and Deep GrabCut, and objectively analyze the experimental results using five evaluation indicators to verify the performance of GrabCut. Finally, some existing problems are pointed out and we also propose some future work.