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Semantic Feature Extraction Based on Video Abstraction and Temporal Modeling

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Pattern Recognition and Image Analysis (IbPRIA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3522))

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Abstract

This paper presents a novel scheme of object-based video indexing and retrieval based on video abstraction and semantic event modeling. The proposed algorithm consists of three major steps; Video Object (VO) extraction, object-based video abstraction and statistical modeling of semantic features. Semantic feature modeling scheme is based on temporal variation of low-level features in object area between adjacent frames of video sequence. Each semantic feature is represented by a Hidden Markov Model (HMM) which characterizes the temporal nature of VO with various combinations of object features. The experimental results demonstrate the effective performance of the proposed approach.

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Author information

Authors and Affiliations

  1. Kongju National University, Kongju, Korea

    Kisung Lee

Authors
  1. Kisung Lee
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Editor information

Editors and Affiliations

  1. Instituto Superior Técnico & Instituto de Sistemas e Robótica, 1049-001, Lisboa, Portugal

    Jorge S. Marques

  2. ETSI Informática y e Telecomunicación, University of Granada, 18071, Granada, Spain

    Nicolás Pérez de la Blanca

  3. CERENA-Centro de Recursos Naturais e Ambiente, Instituto Superior Técnico, Av. Rovisco Pais, 1049-001, Lisboa, Portugal

    Pedro Pina

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© 2005 Springer-Verlag Berlin Heidelberg

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Lee, K. (2005). Semantic Feature Extraction Based on Video Abstraction and Temporal Modeling. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_48

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  • DOI: https://doi.org/10.1007/11492429_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26153-7

  • Online ISBN: 978-3-540-32237-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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