Boundary aware image segmentation with unsupervised mixture models
Thorsten Wilhelm, Christian Wöhler · 2017
Recent image segmentation methods focus mostly on the topic of semantic segmentation and are trained in a supervised fashion. This work proposes a novel and unsupervised bayesian segmentation method, which includes the edges of an image as part of the model. This reduces typical noise patterns in unsupervised segmentation and increases the overall capability of the segmentation. Two ways are proposed to include edges. One way is a passive edge model which grades the segmentation according to a precomputed edge map, and a second variant where this method is used in conjunction with an active edge movement scheme. Both methods are tested on a publicly available dataset, compared to methods from the literature, and the benefit of including edges is emphasized.