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A Multimedia Retrieval Framework Based on Automatic Graded Relevance Judgments

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Advances in Multimedia Modeling (MMM 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7131))

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Abstract

Traditional Content Based Multimedia Retrieval (CBMR) systems measure the relevance of visual samples using a binary scale (Relevant/Non Relevant). However, a picture can be relevant to a semantic category with different degrees, depending on the way such concept is represented in the image. In this paper, we build a CBMR framework that supports graded relevance judgments. In order to quickly build graded ground truths, we propose a measure to reassess binary-labeled databases without involving manual effort: we automatically assign a reliable relevance degree (Non, Weakly, Average, Very Relevant) to each sample, based on its position with respect to the hyperplane drawn by support vector machines in the feature space. We test the effectiveness of our system on two large-scale databases, and we show that our approach outperforms the traditional binary relevance-based frameworks in both scene recognition and video retrieval.

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Author information

Authors and Affiliations

  1. Eurecom, Sophia Antipolis, France

    Miriam Redi & Bernard Merialdo

Authors
  1. Miriam Redi
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  2. Bernard Merialdo
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Editor information

Editors and Affiliations

  1. Institute of Information Technology, Alpen-Adria-Universität Klagenfurt, Universitätsstr. 65-67, 9020, Klagenfurt, Austria

    Klaus Schoeffmann

  2. EURECOM, 2229 Rout des Crêtes, BP 193, 06904, Sophia Antipolis Cedex, France

    Bernard Merialdo

  3. School of Computer Science, Carnegie Mellon University, 5000 Forbes Ave, 15213-3890, Pittsburgh, PA, USA

    Alexander G. Hauptmann

  4. Department of Computer Science, City University of Hong Kong, Tat Chee Ave, Kowloon, Hong Kong

    Chong-Wah Ngo

  5. Department of Electronic and Electrical Engineering, University College London, Roberts Building, Torrington Place, WC1E 7JE, London, UK

    Yiannis Andreopoulos

  6. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Favoritenstrasse 9-11 188/2, 1040, Vienna, Austria

    Christian Breiteneder

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© 2012 Springer-Verlag Berlin Heidelberg

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Redi, M., Merialdo, B. (2012). A Multimedia Retrieval Framework Based on Automatic Graded Relevance Judgments. In: Schoeffmann, K., Merialdo, B., Hauptmann, A.G., Ngo, CW., Andreopoulos, Y., Breiteneder, C. (eds) Advances in Multimedia Modeling. MMM 2012. Lecture Notes in Computer Science, vol 7131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27355-1_29

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  • DOI: https://doi.org/10.1007/978-3-642-27355-1_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27354-4

  • Online ISBN: 978-3-642-27355-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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