We propose a simple
generative model for image segmentation. Like other probabilistic
algorithms (such as EM on a Mixture of Gaussians) the proposed model is
principled, provides both hard and probabilistic cluster assignments,
as well as the ability to naturally incorporate prior knowledge. While
previous probabilistic approaches are restricted to parametric models
of clusters (e.g., Gaussians) we eliminate this limitation. The
suggested approach does not make heavy assumptions on the shape of the
clusters and can thus handle complex structures.
Our experiments show that the suggested approach outperforms previous
work on a variety of image segmentation tasks.
M. Andreetto, L. Zelnik-Manor, and P. Perona,
Probabilistic Image Segmentation", ICCV 2007 (PDF)
image collections (link PDF)
Last update Nov 21, 2007