Toward a Better Integration of Spatial Relations in Learning with Graphical Models

  • Emanuel Aldea
  • Isabelle Bloch
Part of the Studies in Computational Intelligence book series (SCI, volume 292)

Abstract

This paper deals with structural representations of images for machine learning and image categorization. The representation consists of a graph where vertices represent image regions and edges spatial relations between them. Both vertices and edges are attributed. The method is based on graph kernels, in order to derive a metrics for comparing images. We show in particular the importance of edge information (i.e. spatial relations) in the specific context of the influence of the satisfaction or non-satisfaction of a relation between two regions. The main contribution of the paper is situated in highlighting the challenges that follow in terms of image representation, if fuzzy models are considered for estimating relation satisfiability.

Keywords

Image Interpretation Spatial Relations Fuzzy Reasoning Kernel Methods 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Emanuel Aldea
    • 1
  • Isabelle Bloch
    • 1
  1. 1.TSI Department, CNRS UMR 5141 LTCITELECOM ParisTechParisFrance

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