Integration of multiple contextual information for image segmentation using a Bayesian Network

We propose a Bayesian network (BN) model to integrate multiple contextual information and the image measurements for image segmentation. The BN model systematically encodes the contextual relationships between regions, edges and vertices, as well as their image measurements with uncertainties. It allows a principled probabilistic inference to be performed so that image segmentation can be achieved through a most probable explanation (MPE) inference in the BN model. We have achieved encouraging results on the horse images from the Weizmann dataset. We have also demonstrated the possible ways to extend the BN model so as to incorporate other contextual information such as the global object shape and human intervention for improving image segmentation. Human intervention is encoded as new evidence in the BN model. Its impact is propagated through belief propagation to update the states of the whole model. From the updated BN model, new image segmentation is produced.