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2003
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A Self-Referential Perceptual Inference Framework for Video Interpretation

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A Self-Referential Perceptual Inference Framework for Video Interpretation
This paper presents an extensible architectural model for general content-based analysis and indexing of video data which can be customised for a given problem domain. Video interpretation is approached as a joint inference problems which can be solved through the use of modern machine learning and probabilistic inference techniques. An important aspect of the work concerns the use of a novel active knowledge representation methodology based on an ontological query language. This representation allows one to pose the problem of video analysis in terms of queries expressed in a visual language incorporating prior hierarchical knowledge of the syntactic and semantic structure of entities, relationships, and events of interest occurring in a video sequence. Perceptual inference then takes place within an ontological domain defined by the structure of the problem and the current goal set.
Christopher Town, David Sinclair
Added 07 Jul 2010
Updated 07 Jul 2010
Type Conference
Year 2003
Where ICVS
Authors Christopher Town, David Sinclair
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