Interactive Image Segmentation

The goal is to detect an object from the background, when some markers on object(s) and the background are given. As features only probability distributions of the data are used. At first, all the labelled seeds are independently propagated for obtaining homogeneous connected components for each of them. Then the image is divided in blocks, which are classified according to their probabilistic distance from the classified regions. A topographic surface for each class is obtained, using Bayesian dissimilarities and a min-max criterion. Segmentation results on the LHI data set are presented for two algorithms: a regularized classification based on the topographic surface and incorporating an MRF model, and a priority multi-label flooding algorithm.

Click on the thumbnail to view the image and the segmentation result