Integrating expert’s knowledge constraint of time dependent exposures in structure learning for Bayesian networks

2020 ◽  
Vol 107 ◽  
pp. 101874
Author(s):  
Vahé Asvatourian ◽  
Philippe Leray ◽  
Stefan Michiels ◽  
Emilie Lanoy
Keyword(s):  
Bayesian Networks ◽  
Structure Learning ◽  
Time Dependent ◽  
Knowledge Constraint

Bayesian Networks for Image Understanding

2007 ◽  
pp. 128-150
Author(s):  
Andreas Savaki ◽  
Jiebo Luo ◽  
Michael Kane
Keyword(s):  
Bayesian Networks ◽  
Object Detection ◽  
Bayesian Network ◽  
Structure Learning ◽  
Image Understanding ◽  
Semantic Features ◽  
Scene Classification ◽  
Low Level ◽  
Object Parts ◽  
Scene Content

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.

scholarly journals Structure Learning in Bayesian Networks Using Asexual Reproduction Optimization

ETRI Journal ◽  
2011 ◽  
Vol 33 (1) ◽  
pp. 39-49 ◽  
Author(s):  
Ali Reza Khanteymoori ◽  
Mohammad Bagher Menhaj ◽  
Mohammad Mehdi Homayounpour
Keyword(s):  
Bayesian Networks ◽  
Asexual Reproduction ◽  
Structure Learning

New skeleton-based approaches for Bayesian structure learning of Bayesian networks

2013 ◽  
Vol 13 (2) ◽  
pp. 1110-1120 ◽  
Author(s):  
Andrés R. Masegosa ◽  
Serafín Moral
Keyword(s):  
Bayesian Networks ◽  
Structure Learning
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