Abstract
This chapter introduces probabilistic graphical models as a statistical–structural pattern recognition paradigm. Many pattern recognition problems can be posed as labeling problems to which the solution is a set of linguistic labels assigned to extracted features from speech signals, image pixels, and image regions. Graphical models use Markov properties to measure a local probability on the labels within the neighborhood system. The Bayesian decision theory guarantees the best labeling configuration according to the maximum a posteriori criterion.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, New York (2005)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer-Verlag, New York (2001)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)
Ross, T.J., Booker, J.M., Parkinson, W.J.: Fuzzy Logic and Probability Applications: Bridging the Gap, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2002)
Zhu, S.C.: Statistical modeling and conceptualization of visual patterns. IEEE Trans. Pattern Anal. Machine Intell. 25(6), 691–712 (2003)
Guo, C.E., Zhu, S.C., Wu, Y.N.: Modeling visual patterns by integrating descriptive and generative methods. International Journal of Computer Vision 53(1), 5–29 (2003)
Murphy, K.: A brief introduction to graphical models and Bayesian networks (1998). http://www.cs.ubc.ca/ murphyk/Bayes/bnintro.html
Smyth, P.: Belief networks, hidden Markov models, and Markov random fields: a unifying view. Pattern Recognition Letters 18(11–13), 1261–1268 (1997)
Lauritzen, S.L.: Graphical Models. Clarendon Press, Oxford (1996)
Zeng, J., Liu, Z.Q.: Markov random fields-based statistical character structure modeling for Chinese character recognition. IEEE Trans. Pattern Anal. Machine Intell. 30(5), 767–780 (2008)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2015 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Zeng, J., Liu, ZQ. (2015). Probabilistic Graphical Models. In: Type-2 Fuzzy Graphical Models for Pattern Recognition. Studies in Computational Intelligence, vol 591. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44690-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-44690-4_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44689-8
Online ISBN: 978-3-662-44690-4
eBook Packages: EngineeringEngineering (R0)