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Computer vision techniques have made considerable progress in recognizing object categories by learning models that normally rely on a set of discriminative features. However, a drawback of those models is that, in contrast to human perception that makes extensive use of logic-based rules, they fail to benefit from knowledge that is provided explicitly. In this paper, the authors propose a framework that is able to perform knowledge-assisted analysis of visual content. They use ontologies to model domain knowledge and a set of conditional probabilities to model the application context. Then, a Bayesian Network (BN) is used for integrating statistical and explicit knowledge and perform hypothesis testing using evidence-driven probabilistic inference.
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