Image interpretation by combining ontologies and bayesian networks

Authors:
Spiros Nikolopoulos;Georgios Th. Papadopoulos;Ioannis Kompatsiaris;Ioannis Patras
Affiliations:
CERTH-ITI, Informatics and Telematics Institute, Greece,School of Electronic Engineering and Computer Science, QMUL, UK;CERTH-ITI, Informatics and Telematics Institute, Greece;CERTH-ITI, Informatics and Telematics Institute, Greece;School of Electronic Engineering and Computer Science, QMUL, UK
Venue:
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
Year:
2012

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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.