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Interactive image segmentation is a topic of many studies in imageprocessing. In a conventional approach, a user marks some pixels of theobject(s) of interest and background, and an algorithm propagates these labelsto the rest of the image. This paper presents a new graph-based method forinteractive segmentation with two stages. In the first stage, nodesrepresenting pixels are connected to their $k$-nearest neighbors to build acomplex network with the small-world property to propagate the labels quickly.In the second stage, a regular network in a grid format is used to refine thesegmentation on the object borders. Despite its simplicity, the proposed methodcan perform the task with high accuracy. Computer simulations are performedusing some real-world images to show its effectiveness in both two-classes andmulti-classes problems. It is also applied to all the images from the MicrosoftGrabCut dataset for comparison, and the segmentation accuracy is comparable tothose achieved by some state-of-the-art methods, while it is faster than them.In particular, it outperforms some recent approaches when the user input iscomposed only by a few "scribbles" draw over the objects. Its computationalcomplexity is only linear on the image size at the best-case scenario andlinearithmic in the worst case.

Interactive Image Segmentation using Label Propagation through Complex Networks