Learning a Model for Shape-Constrained Image Segmentation from Weakly Labeled Data.
In the paper we address a challenging problem of incorporating preferences on possible shapes of an object in a binary image segmentation framework. We extend the well-known conditional random fields model by adding new variables that are responsible for the shape of an object. We describe the shape via a flexible graph augmented with vertex positions and edge widths. We derive exact and approximate algorithms for MAP estimation of label and shape variables given an image. An original learning procedure for tuning parameters of our model based on unlabeled images with only shape descriptions given is also presented. Experiments confirm that our model improves the segmentation quality in hard-to-segment images by taking into account the knowledge about typical shapes of the object.