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Linguistic Estimation of Topic Difficulty in Cross-Language Image Retrieval

  • Michael Grubinger
  • Clement Leung
  • Paul Clough
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)

Abstract

Selecting suitable topics in order to assess system effectiveness is a crucial part of any benchmark, particularly those for retrieval systems. This includes establishing a range of example search requests (or topics) in order to test various aspects of the retrieval systems under evaluation. In order to assist with selecting topics, we present a measure of topic difficulty for cross-language image retrieval. This measure has enabled us to ground the topic generation process within a methodical and reliable framework for ImageCLEF 2005. This document describes such a measure for topic difficulty, providing concrete examples for every aspect of topic complexity and an analysis of topics used in the ImageCLEF 2003, 2004 and 2005 ad-hoc task.

Keywords

Image Retrieval Query Expansion Mean Average Precision Translation Quality Search Request 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Michael Grubinger
    • 1
  • Clement Leung
    • 1
  • Paul Clough
    • 2
  1. 1.School of Computer Science and MathematicsVictoria UniversityMelbourneAustralia
  2. 2.Department of Information StudiesSheffield UniversitySheffieldUK

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