Bayesian reasoning on qualitative descriptions from images and speech
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摘要
Image understanding denotes not only the ability to extract specific, non-numerical information from images, but it implies also reasoning about the extracted information. We propose a qualitative representation for image understanding results, which is suitable for reasoning with Bayesian networks. Our qualitative representation is enhanced with probabilistic information to represent uncertainties and errors in the understanding of noisy sensory data. The probabilistic information is supplied to a Bayesian network in order to find the most plausible interpretation. We apply this approach for the integration of image and speech understanding in a scenario where we want to find objects in a visually observed scene which are verbally described by a human. Results demonstrate the performance of our approach.
论文关键词:Image understanding,Bayesian networks,Spatial relations,Qualitative description
论文评审过程:Received 18 September 1997, Revised 18 December 1998, Accepted 13 July 1999, Available online 12 January 2000.
论文官网地址:https://doi.org/10.1016/S0262-8856(99)00024-4