Image Interpretation by Combining Ontologies and Bayesian Networks

Spiros Nikolopoulos^{1, 2}, Georgios Th. Papadopoulos¹, Ioannis Kompatsiaris¹, and Ioannis Patras²

¹CERTH-ITI, Informatics and Telematics Institute, Greece
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²School of Electronic Engineering and Computer Science, QMUL, UK
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Abstract. 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.

LNAI 7297, p. 307 ff.

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© Springer-Verlag Berlin Heidelberg 2012