Search (2 results, page 1 of 1)

  • × author_ss:"Clough, P."
  • × year_i:[2000 TO 2010}
  1. Clough, P.; Sanderson, M.: User experiments with the Eurovision Cross-Language Image Retrieval System (2006) 0.01 
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    Abstract
    In this article the authors present Eurovision, a textbased system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To the authors' knowledge, this is the first published set of user experiments for CL image retrieval. They show that (a) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (b) categorizing images assists the user's search, and (c) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, they describe important aspects of any CL image retrieval system.
    Footnote
    Beitrag einer special topic section on multilingual information systems
    Source
    Journal of the American Society for Information Science and Technology. 57(2006) no.5, S.697-708
  2. Rorissa, A.; Clough, P.; Deselaers, T.: Exploring the relationship between feature and perceptual visual spaces (2008) 0.00 
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    Abstract
    The number and size of digital repositories containing visual information (images or videos) is increasing and thereby demanding appropriate ways to represent and search these information spaces. Their visualization often relies on reducing the dimensions of the information space to create a lower-dimensional feature space which, from the point-of-view of the end user, will be viewed and interpreted as a perceptual space. Critically for information visualization, the degree to which the feature and perceptual spaces correspond is still an open research question. In this paper we report the results of three studies which indicate that distance (or dissimilarity) matrices based on low-level visual features, in conjunction with various similarity measures commonly used in current CBIR systems, correlate with human similarity judgments.
    Source
    Journal of the American Society for Information Science and Technology. 59(2008) no.5, S.770-784

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