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A Bayesian network-based framework for semantic image understanding

Authors:

Andreas E. Savakis,

Amit SinghalAuthors Info & Claims

Pattern Recognition, Volume 38, Issue 6

Pages 919 - 934

https://doi.org/10.1016/j.patcog.2004.11.001

Published: 01 June 2005 Publication History

Abstract

Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems.

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Villa‐Blanco CLarrañaga PBielza C(2021)Multidimensional continuous time Bayesian network classifiersInternational Journal of Intelligent Systems10.1002/int.2261136:12(7839-7866)Online publication date: 26-Oct-2021
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Mercy Rajaselvi Beaulah PManjula DSugumaran V(2018)Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and AnnotationJournal of Medical Systems10.1007/s10916-018-0986-642:7(1-15)Online publication date: 1-Jul-2018
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A Bayesian network-based framework for semantic image understanding
1. Computing methodologies
  1. Artificial intelligence
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      1. Computer vision tasks
  2. Machine learning
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      1. Supervised learning
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Information

Published In

cover image Pattern Recognition

Pattern Recognition Volume 38, Issue 6

June, 2005

162 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Pattern Recognition Society © 2005.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 June 2005

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Cited By

Villa‐Blanco CLarrañaga PBielza C(2021)Multidimensional continuous time Bayesian network classifiersInternational Journal of Intelligent Systems10.1002/int.2261136:12(7839-7866)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1002/int.22611
Mercy Rajaselvi Beaulah PManjula DSugumaran V(2018)Categorization of Images Using Autoencoder Hashing and Training of Intra Bin Classifiers for Image Classification and AnnotationJournal of Medical Systems10.1007/s10916-018-0986-642:7(1-15)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s10916-018-0986-6
Coombes MEaton WChen W(2017)Machine Vision for UAS Ground OperationsJournal of Intelligent and Robotic Systems10.1007/s10846-017-0542-588:2-4(527-546)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s10846-017-0542-5
Ropero RRumí RAguilera P(2016)Modelling uncertainty in social-natural interactionsEnvironmental Modelling & Software10.1016/j.envsoft.2014.07.00875:C(362-372)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1016/j.envsoft.2014.07.008
Taal ASherer JBent KFedeles E(2016)Cognitive computing and proposed approaches to conceptual organization of case law knowledge basesArtificial Intelligence and Law10.1007/s10506-016-9188-z24:4(347-370)Online publication date: 1-Dec-2016
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Ri CYao M(2015)Bayesian network based semantic image classification with attributed relational graphMultimedia Tools and Applications10.1007/s11042-014-1858-974:13(4965-4986)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1007/s11042-014-1858-9
Sun LGe HYoshida SLiang YTan G(2014)Support vector description of clusters for content-based image annotationPattern Recognition10.1016/j.patcog.2013.10.01547:3(1361-1374)Online publication date: 1-Mar-2014
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Fan WBouguila N(2014)Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and ApplicationsNeural Processing Letters10.1007/s11063-013-9293-x39:2(115-135)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1007/s11063-013-9293-x
Li YSawhne RWilck J(2013)Applying Bayesian Network Techniques to Prioritize Lean Six Sigma EffortsInternational Journal of Strategic Decision Sciences10.4018/jsds.20130401014:2(1-15)Online publication date: 1-Apr-2013
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Fan WBouguila N(2013)Variational learning of a Dirichlet process of generalized Dirichlet distributions for simultaneous clustering and feature selectionPattern Recognition10.1016/j.patcog.2013.03.02646:10(2754-2769)Online publication date: 1-Oct-2013
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