Semi-supervised Image Segmentation by Parametric Distributional Clustering
The problem of semi-supervised image segmentation is frequently posed e.g. in remote sensing applications. In this setting, one aims at finding a decomposition of a given image into its constituent regions, which are typically assumed to have homogeneously distributed pixel values. In addition, it is requested that these regions can be equipped with some semantics, i.e. that they can be matched to particular land cover classes. For this purpose, class labels are provided for a small subset of the image data. The demand that the image segmentation respects those class labels implies that the segmentation algorithm should be posed as a constrained optimization problem.