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A Bayesian Network Approach to Multi-feature Based Image Retrieval

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Semantic Multimedia (SAMT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4306))

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Abstract

This paper aims at devising a Bayesian Network approach to object centered image retrieval employing non-monotonic inference rules and combining multiple low-level visual primitives as cue for retrieval. The idea is to model a global knowledge network by treating an entire image as a scenario. The overall process is divided into two stages: the initial retrieval stage which is concentrated on finding an optimal multi-feature space stage and doing a simple initial retrieval within this space; and the Bayesian inference stage which uses the initial retrieval information and seeks for a more precise second- retrieval.

The work leading to this paper was partially supported by the European Commission under contracts FP6-001765 aceMedia and FP6-027026 K-Space.

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Author information

Authors and Affiliations

  1. Department of Electronic Engineering, Queen Mary, University of London, London, U.K.

    Qianni Zhang & Ebroul Izquierdo

Authors
  1. Qianni Zhang
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  2. Ebroul Izquierdo
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Editor information

Editors and Affiliations

  1. Image, Video and Multimedia Systems Laboratory, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 157 80, Athens, Greece

    Yannis Avrithis

  2. Informatics and Telematics Institute, Centre for Research and Technology-Hellas, 57001, Thessaloniki, Greece

    Yiannis Kompatsiaris

  3. Fachbereich Informatik, Universität Koblenz-Landau, Universitätsstraße 1, 56070, Koblenz, Germany

    Steffen Staab

  4. Centre for Digital Video Processing, Adaptive Information Cluster, Dublin City University, Ireland

    Noel E. O’Connor

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© 2006 Springer-Verlag Berlin Heidelberg

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Zhang, Q., Izquierdo, E. (2006). A Bayesian Network Approach to Multi-feature Based Image Retrieval. In: Avrithis, Y., Kompatsiaris, Y., Staab, S., O’Connor, N.E. (eds) Semantic Multimedia. SAMT 2006. Lecture Notes in Computer Science, vol 4306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11930334_11

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  • DOI: https://doi.org/10.1007/11930334_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49335-8

  • Online ISBN: 978-3-540-49337-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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