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.

• Type: Conference
• Year: 2003
• Where: ICVS
• Authors: Christopher Town, David Sinclair
• Keywords: ICVS 2003, Inference, Joint Inference Problems, Probabilistic Inference Techniques