A Poset Based Approach for Condition Weighting

  • David Zellhöfer
  • Ingo Schmitt
Conference paper

DOI: 10.1007/978-3-642-14758-6_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)
Cite this paper as:
Zellhöfer D., Schmitt I. (2010) A Poset Based Approach for Condition Weighting. In: Detyniecki M., Leiner U., Nürnberger A. (eds) Adaptive Multimedia Retrieval. Identifying, Summarizing, and Recommending Image and Music. AMR 2008. Lecture Notes in Computer Science, vol 5811. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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 

Personalised recommendations