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Multimedia Tools and Applications

, Volume 49, Issue 1, pp 167–194 | Cite as

Investigating fuzzy DLs-based reasoning in semantic image analysis

  • Stamatia Dasiopoulou
  • Ioannis Kompatsiaris
  • Michael G. Strintzis
Article

Abstract

Recent advances in semantic image analysis have brought forth generic methodologies to support concept learning at large scale. The attained performance however is highly variable, reflecting effects related to similarities and variations in the visual manifestations of semantically distinct concepts, much as to the limitations issuing from considering semantics solely in the form of perceptual representations. Aiming to enhance performance and improve robustness, we investigate a fuzzy DLs-based reasoning framework, which enables the integration of scene and object classifications into a semantically consistent interpretation by capturing and utilising the underlying semantic associations. Evaluation with two sets of input classifiers, configured so as to vary with respect to the wealth of concepts’ interrelations, outlines the potential of the proposed approach in the presence of semantically rich associations, while delineating the issues and challenges involved.

Keywords

Fuzzy reasoning Semantic image analysis Semantic integration Fuzzy DLs Inconsistency handling 

Notes

Acknowledgements

This work was partially supported by the European Commission under contracts FP6-001765 aceMedia, FP6-507482 KnowledgeWeb and FP7-215453 WeKnowIt.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Stamatia Dasiopoulou
    • 1
  • Ioannis Kompatsiaris
    • 1
  • Michael G. Strintzis
    • 1
  1. 1.Informatics and Telematics InstituteCentre for Research and Technology HellasThessalonikiGreece

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