PROBABILISTIC GRAPHICAL MODELS AND THEIR APPLICATIONS IN COMPUTER VISION

Probabilistic graphical models (PGMs) have become increasingly popular statistical modeling tools for effectively addressing many real-world problems. Both directed graphical models such as Bayesian Networks (BNs), Dynamic Bayesian Networks (DBNs), and undirected graphical models such as Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) have been widely used to solve computer vision problems. In this chapter, we briefly introduce the basic concepts and theories of different types of PGMs, including BNs, DBNs, MRFs, and CRFs. We further show their applications in two important computer vision problems: facial expression recognition and image segmentation. Specifically, we first illustrate how to employ a DBN to capture the static and dynamic relationships between action units for facial expression recognition. We then introduce a hybrid PGM to model the heterogeneous relationships between different image entities (regions, edges, junctions, etc.) for effective image segmentation. These applications demonstrate the powerful capability of PGMs for flexibly modeling the real-world problems in a rigorous statistical framework and for solving the computer vision problems systematically through a probabilistic inference.