Bayesian Networks for Image Understanding
Andreas Savaki (Rochester Institute of Technology, USA), Jiebo Luo (Kodak Research Laboratories, USA) and Michael Kane (Yale University, USA)
Copyright: © 2007
Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that Bayesian networks are particularly well-suited for image understanding problems, and present case studies in indoor-outdoor scene classification and parts-based object detection. First, improved scene classification is accomplished using both low-level features, such as color and texture, and semantic features, such as the presence of sky and grass. Integration of low-level and semantic features is achieved using a Bayesian network framework. The network structure can be determined by expert opinion or by automated structure learning methods. Second, object detection at multiple views relies on a parts-based approach, where specialized detectors locate object parts and a Bayesian network acts as the arbitrator in order to determine the object presence. In general, Bayesian networks are found to be powerful integrators of different features and help improve the performance of image understanding systems.