A multiscale hybrid model exploiting heterogeneous contextual relationships for image segmentation
We propose a framework that can conveniently capture heterogeneous relationships among multiple random variables. The framework is formulated based on a hybrid probabilistic graphical model. It allows using both directed links and undirected links to capture various types of relationships. Based on this framework, we develop a multiscale hybrid model for image segmentation. The multiscale model systematically captures the spatial relationships and causal relationships among such image entities as regions, edges, and vertices at different scales. We further show how to parameterize such a hybrid model and how to factorize its joint probability distribution according to the global Markov properties. Based on this factorization, we exploit the factor graph theory to perform joint probabilistic inference and solve for the image segmentation problem.