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Combining Statistical and Symbolic Reasoning for Active Scene Categorization

  • Thomas Reineking
  • Niclas Schult
  • Joana Hois
Part of the Communications in Computer and Information Science book series (CCIS, volume 128)

Abstract

One of the reasons why humans are so successful at interpreting everyday situations is that they are able to combine disparate forms of knowledge. Most artificial systems, by contrast, are restricted to a single representation and hence fail to utilize the complementary nature of multiple sources of information. In this paper, we introduce an information-driven scene categorization system that integrates common sense knowledge provided by a domain ontology with a learned statistical model in order to infer a scene class from recognized objects. We show how the unspecificity of coarse logical constraints and the uncertainty of statistical relations and the object detection process can be modeled using Dempster-Shafer theory and derive the resulting belief update equations. In addition, we define an uncertainty minimization principle for adaptively selecting the most informative object detectors and present classification results for scenes from the LabelMe image database.

Keywords

Recognition Rate Object Detection Description Logic Object Class Domain Ontology 
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 2011

Authors and Affiliations

  • Thomas Reineking
    • 1
  • Niclas Schult
    • 1
  • Joana Hois
    • 2
  1. 1.Cognitive NeuroinformaticsUniversity of BremenBremenGermany
  2. 2.Research Center on Spatial Cognition SFB/TR 8University of BremenBremenGermany

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