  Many interactive image segmentation techniques are based on semi-supervised
learning. The user may label some pixels from each object and the SSL algorithm
will propagate the labels from the labeled to the unlabeled pixels, finding
object boundaries. This paper proposes a new SSL graph-based interactive image
segmentation approach, using undirected and unweighted kNN graphs, from which
the unlabeled nodes receive contributions from other nodes (either labeled or
unlabeled). It is simpler than many other techniques, but it still achieves
significant classification accuracy in the image segmentation task. Computer
simulations are performed using some real-world images, extracted from the
Microsoft GrabCut dataset. The segmentation results show the effectiveness of
the proposed approach.
