Image Interpretation by Combining Ontologies and Bayesian Networks

  • Spiros Nikolopoulos
  • Georgios Th. Papadopoulos
  • Ioannis Kompatsiaris
  • Ioannis Patras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)


A drawback of current computer vision techniques is that, in contrast to human perception that makes use of logic-based rules, they fail to benefit from knowledge that is provided explicitly. In this work we propose a framework that performs knowledge-assisted analysis of visual content using ontologies to model domain knowledge and conditional probabilities to model the application context. A bayesian network (BN) is used for integrating statistical and explicit knowledge and perform hypothesis testing using evidence-driven probabilistic inference. Our results show significant improvements compared to a baseline approach that does not make any use of context or domain knowledge.


Bayesian Network Domain Knowledge Category Concept Application Context Probabilistic Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Spiros Nikolopoulos
    • 1
    • 2
  • Georgios Th. Papadopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  • Ioannis Patras
    • 2
  1. 1.CERTH-ITI, Informatics and Telematics InstituteGreece
  2. 2.School of Electronic Engineering and Computer ScienceQMULUK

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