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A Study into Annotation Ranking Metrics in Community Contributed Image Corpora

  • Mark Hughes
  • Gareth J. F. Jones
  • Noel E. O’ConnorEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8382)

Abstract

Community contributed datasets are becoming increasing common in automated image annotation systems. One important issue with community image data is that there is no guarantee that the associated metadata is relevant. A method is required that can accurately rank the semantic relevance of community annotations. This should enable the extracting of relevant subsets from potentially noisy collections of these annotations. Having relevant, non-heterogeneous tags assigned to images should improve community image retrieval systems, such as Flickr, which are based on text retrieval methods. In the literature, the current state of the art approach to ranking the semantic relevance of Flickr tags is based on the widely used tf-idf metric. In the case of datasets containing landmark images, however, this metric is inefficient and can be improved upon. In this paper, we present a landmark recognition framework, that provides end-to-end automated recognition and annotation. In our study into automated annotation, we evaluate 5 alternate approaches to tf-idf to rank tag relevance in community contributed landmark image corpora. We carry out a thorough evaluation of each of these ranking metrics and results of this evaluation demonstrate that four of these proposed techniques outperform the current commonly-used tf-idf approach for this task. Our best performing evaluated approach achieves a significant F-Measure increase of .19 over tf-idf.

Keywords

Image annotation Landmark recognition Tag relevance 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mark Hughes
    • 1
  • Gareth J. F. Jones
    • 2
  • Noel E. O’Connor
    • 1
    Email author
  1. 1.CLARITY: Centre for Sensor Web TechnologiesDublin City UniversityDublin 9Ireland
  2. 2.Centre for Next Generation LocalisationDublin City UniversityDublin 9Ireland

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