Multilingual Information Access Evaluation II. Multimedia Experiments

Volume 6242 of the series Lecture Notes in Computer Science pp 133-141

University of Glasgow at ImageCLEFPhoto 2009: Optimising Similarity and Diversity in Image Retrieval

  • Teerapong LeelanupabAffiliated withUniversity of Glasgow
  • , Guido ZucconAffiliated withUniversity of Glasgow
  • , Anuj GoyalAffiliated withUniversity of Glasgow
  • , Martin HalveyAffiliated withUniversity of Glasgow
  • , P. PunithaAffiliated withUniversity of Glasgow
  • , Joemon M. JoseAffiliated withUniversity of Glasgow

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In this paper we describe the approaches adopted to generate the runs submitted to ImageCLEFPhoto 2009 with an aim to promote document diversity in the rankings. Four of our runs are text based approaches that employ textual statistics extracted from the captions of images, i.e. MMR [1] as a state of the art method for result diversification, two approaches that combine relevance information and clustering techniques, and an instantiation of Quantum Probability Ranking Principle. The fifth run exploits visual features of the provided images to re-rank the initial results by means of Factor Analysis. The results reveal that our methods based on only text captions consistently improve the performance of the respective baselines, while the approach that combines visual features with textual statistics shows lower levels of improvements.