Simple Interactive Image Segmentation using Label Propagation through kNN graphs
This is an incremental improvement for users needing efficient image segmentation tools.
The paper tackles interactive image segmentation by proposing a simpler graph-based semi-supervised learning method using kNN graphs, achieving significant classification accuracy in tests on the Microsoft GrabCut dataset.
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.