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Overview of Approaches to Semantic Augmentation of Multimedia Databases for Efficient Access and Content Retrieval

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Adaptive Multimedia Retrieval (AMR 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3094))

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Abstract

Ever-increasing amount of multimedia available online necessitates the development of new techniques and methods that can overcome the semantic gap problem. The said problem, encountered due to major disparities between inherent representational characteristics of multimedia and its semantic content sought by the user, has been a prominent research direction addressed by a great number of semantic augmentation approaches originating from such areas as machine learning, statistics, natural language processing, etc. In this paper, we review several of these recently developed techniques that bring together low-level representation of multimedia and its semantics in order to improve the efficiency of access and retrieval. We also present a distance-based discriminant analysis (DDA) method that defines the design of a basic building block classifier for distinguishing among a selected number of semantic categories. In addition to that, we demonstrate how a set of DDA classifiers can be grouped into a hierarchical ensemble for prediction of an arbitrary set of semantic classes.

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Author information

Authors and Affiliations

  1. Computer Vision and Multimedia Lab, University of Geneva, 24 Rue du General-Dufour, CH-1211, Geneva 4, Switzerland

    Serhiy Kosinov & Stéphane Marchand-Maillet

Authors
  1. Serhiy Kosinov
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  2. Stéphane Marchand-Maillet
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Editor information

Editors and Affiliations

  1. Institute for Knowledge and Language Engineering, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, D-39106, Magdeburg, Germany

    Andreas Nürnberger

  2. Laboratoire d’Informatique de Paris 6,  

    Marcin Detyniecki

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Kosinov, S., Marchand-Maillet, S. (2004). Overview of Approaches to Semantic Augmentation of Multimedia Databases for Efficient Access and Content Retrieval. In: Nürnberger, A., Detyniecki, M. (eds) Adaptive Multimedia Retrieval. AMR 2003. Lecture Notes in Computer Science, vol 3094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25981-7_2

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  • DOI: https://doi.org/10.1007/978-3-540-25981-7_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22163-0

  • Online ISBN: 978-3-540-25981-7

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