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A Self-Referential Perceptual Inference Framework for Video Interpretation

  • Christopher Town
  • David Sinclair
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2626)

Abstract

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.

Keywords

Video Analysis Perceptual Inference Content Base Image Retrieval Ontological Language Symbol Grounding Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Christopher Town
    • 1
  • David Sinclair
    • 2
  1. 1.University of Cambridge Computer LaboratoryCambridgeUK
  2. 2.Waimara LtdCambridgeUK

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