Using Relevance Feedback to Bridge the Semantic Gap

  • Ebroul Izquierdo
  • Divna Djordjevic
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3877)


In this article relevant developments in relevance feedback based image annotation and retrieval are reported. A new approach to infer semantic concepts representing meaningful objects in images is also described. The proposed technique combines user relevance feedback and underlying low-level properties of elementary building blocks making up semantic objects in images. Images are regarded as mosaics made of small building blocks featuring good representations of colour, texture and edgeness. The approach is based on accurate classification of these building blocks. Once this has been achieved, a signature for the object of concern is built. It is expected that this signature features a high discrimination power and consequently it becomes very suitable to find other images containing the same semantic object. The model combines fuzzy clustering and relevance feedback in the training stage, and uses fuzzy support vector machines in the generalization stage.


Feature Space Image Retrieval Relevance Feedback Semantic Concept Content Base Image Retrieval 
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

  • Ebroul Izquierdo
    • 1
  • Divna Djordjevic
    • 1
  1. 1.Queen Mary University of LondonLondonUK

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