A Markov Random Field based image segmentation system which combines top-down and bottom-up segmentation approaches is proposed in this study. The system is especially proposed for applications where no labeled training set is available, 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. This information is integrated to the segmentation process 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.