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. © 2012 Springer-Verlag.
CITATION STYLE
Nikolopoulos, S., Papadopoulos, G. T., Kompatsiaris, I., & Patras, I. (2012). Image interpretation by combining ontologies and Bayesian networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7297 LNCS, pp. 307–314). https://doi.org/10.1007/978-3-642-30448-4_39
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