Integration of Domain Specific Information in the Form of Color Homogeneity into MRF Based Image Segmentation

We propose a Markov Random Field based image segmentation method which integrates domain specific information into MRF energy. The proposed segmentation method assumes that there is no labeled training set, but some priori general information referred as domain specific information about the dataset, is available. Domain specific information is received from a domain expert and formalized by a mathematical representation. The type of information and its representation depends on the content of the image dataset to be segmented. The proposed method, combines top-down and bottom-up segmentation approaches by associating the domain specific information into the MRF energy function in an unsupervised framework. Due to the inclusion of domain specific information, this approach can be considered as a first step to semantic image segmentation under an unsupervised MRF model. The proposed system is compared with the state of the art unsupervised image segmentation methods quantitatively via two evaluation metrics; consistency error and probabilistic rand index and satisfactory results are obtained.