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
