An automatic initialization of interactive segmentation methods using shortest path basins

Abstract

Image segmentation is one of many fundamental problems in computer vision. The need to divide an image to a number of classes is often a part of a system that uses image processing methods. Therefore, lots of methods were developed that are based on different approaches. The image segmentation could be classified with respect to many criteria. One such a criterion is based on the degree of allowed interactivity. The interactivity could be of several types—interactive initialization, interaction while the computation is running or manual refinement of achieved results, for example. Especially the precise initialization plays an important role in many methods. Therefore the possibility to initialize the method manually is often invaluable advantage and information obtained this way could be the difference between good and poor results. Unfortunately, in many cases it is not possible to initialize a method manually and the process needs to be automated. In this paper, an approach for such an automation is presented. It is based on shortest paths in a graph and deriving an area of influence for each obtained seed point. This method is called shortest path basins.