Clustering-based Image Segmentation using Automatic GrabCut

Abstract

GrabCut is one of the most powerful semi-automatic segmentation techniques. One main drawback of GrabCut is the need for user interaction in order to initialize the segmentation process. User interaction involves dragging a rectangle around the object of interest in the image to extract it. This restricts GrabCut for binary-label segmentation, where the image cannot be segmented into more than two; foreground and background segments. Unsupervised clustering is a powerful automatic tool for dividing images into a specified number of regions based on defined image features. The authors had introduced the SOFM clustering technique for GrabCut automation as a replacement to the user interaction. In this paper, K-means and Fuzzy C-means are introduced as new clustering techniques to automate GrabCut and improve the segmentation accuracy. Experimental results and comparisons with the previous results are carried out to test the efficiency of the proposed techniques in terms of segmentation quality and accuracy.