Welcome to the InfoSci Platform
Bayesian Networks for Image Understanding

Bayesian Networks for Image Understanding

Andreas Savaki, Jiebo Luo, Michael Kane
Copyright: © 2007 |Pages: 23
ISBN13: 9781599041414|ISBN10: 1599041413|EISBN13: 9781599041438
DOI: 10.4018/978-1-59904-141-4.ch007
Cite Chapter Cite Chapter

MLA

Savaki, Andreas, et al. "Bayesian Networks for Image Understanding." Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, IGI Global, 2007, pp. 128-150. https://doi.org/10.4018/978-1-59904-141-4.ch007

APA

Savaki, A., Luo, J., & Kane, M. (2007). Bayesian Networks for Image Understanding. In A. Mittal & A. Kassim (Eds.), Bayesian Network Technologies: Applications and Graphical Models (pp. 128-150). IGI Global. https://doi.org/10.4018/978-1-59904-141-4.ch007

Chicago

Savaki, Andreas, Jiebo Luo, and Michael Kane. "Bayesian Networks for Image Understanding." In Bayesian Network Technologies: Applications and Graphical Models, edited by Ankush Mittal and Ashraf Kassim, 128-150. Hershey, PA: IGI Global, 2007. https://doi.org/10.4018/978-1-59904-141-4.ch007

Export Reference

Mendeley
Favorite

Abstract

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.