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A Self-Referential Perceptual Inference Framework for Video Interpretation

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Computer Vision Systems (ICVS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2626))

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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.

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Author information

Authors and Affiliations

  1. University of Cambridge Computer Laboratory, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK

    Christopher Town

  2. Waimara Ltd, 115 Ditton Walk, Cambridge, UK

    David Sinclair

Authors
  1. Christopher Town
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  2. David Sinclair
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Editor information

Editors and Affiliations

  1. INRIA Rhône-Alpes, 655 Ave de l’Europe, 38330, Montbonnot, France

    James L. Crowley

  2. Montefiore Institute, University of Liège, 4000, Liège Sart-Tilman, Belgium

    Justus H. Piater

  3. Automation and Control Institute, Vienna University of Technology, Gusshausstraße 27/376, 1040, Vienna, Austria

    Markus Vincze

  4. Institute of Digital Image Processing, Joanneum Research, Wastiangasse 6, 8010, Graz, Austria

    Lucas Paletta

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© 2003 Springer-Verlag Berlin Heidelberg

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Town, C., Sinclair, D. (2003). A Self-Referential Perceptual Inference Framework for Video Interpretation. In: Crowley, J.L., Piater, J.H., Vincze, M., Paletta, L. (eds) Computer Vision Systems. ICVS 2003. Lecture Notes in Computer Science, vol 2626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36592-3_6

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  • DOI: https://doi.org/10.1007/3-540-36592-3_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00921-4

  • Online ISBN: 978-3-540-36592-1

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