A Poset Based Approach for Condition Weighting

  • David Zellhöfer
  • Ingo Schmitt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)


Current research in multimedia retrieval (MR) does not satisfactorily mirror research results from psychology revealing a different significance of certain characteristics of a media object to a query in terms of similarity. Although the relevance of user-controlled condition weights has been demonstrated, there is a lack of systems supporting users in setting these weights.

In this work, we present a relevance feedback based approach that supports users to set condition weights in order to retrieve results from the MR system that are consistent with their perception of similarity. Condition weights are learned by a machine based learning algorithm from user preferences based on a partially ordered set.


Condition Weight Boolean Logic Atomic Condition Relevance Feedback Media Object 
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 2010

Authors and Affiliations

  • David Zellhöfer
    • 1
  • Ingo Schmitt
    • 1
  1. 1.Department of Computer Science Database and Information Systems GroupBTU Cottbus 

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