An Adaptive Recursive Reconstruction Technique for Segmentation of Images

Image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). Image segmentation is typically used to locate objects and boundaries (lines, curves, etc. ) in images. Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic (s). Among the various approaches proposed for this task, unsupervised methods have the advantage of being able to segment images without any assistance from the user. However, such methods often suffer from long runtimes and tend to be sensitive to the choice of parameters. Because of these problems, users will often prefer semi-supervised methods, which provide a more controllable output in the same amount of time. This paper proposes a new unsupervised approach, based on random walks, which maps each pixel to the most probable label in a local neighbourhood. To make this approach more robust to the choice and learning of the parameters, we propose an efficient computational technique, in which the parameters and the segmentation probabilities are recomputed alternatively. We also describe a refinement strategy that improves the speed and accuracy of the segmentation by applying random walks at different scales.