Imagination: Exploiting Link Analysis for Accurate Image Annotation

  • Ilaria Bartolini
  • Paolo Ciaccia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4918)


The advent of digital photography calls for effective techniques for managing growing amounts of color images. Systems that only rely on low-level image features are nowadays limited by the semantic gap problem, which leads to a mismatch between the user subjective notion of similarity and the one adopted by a system. A possible way to reduce the semantic gap is to (semi-)automatically assign meaningful terms to images, so as to enable a high-level, concept-based, retrieval. In this paper we explore the opportunities offered by graph-based link analysis techniques in the development of a semi-automatic image captioning system. The approach we propose is appealing since the predicted terms for an image are in variable number, depending on the image content, represent correlated terms, and can also describe abstract concepts. We present preliminary results on our prototype system and discuss possible extensions.


Near Neighbor Query Image Steady State Probability Image Annotation Correlation Threshold 
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 2008

Authors and Affiliations

  • Ilaria Bartolini
    • 1
  • Paolo Ciaccia
    • 1
  1. 1.DEISUniversity of BolognaItaly

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