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Kernel Fusion for Image Classification Using Fuzzy Structural Information

  • Emanuel Aldea
  • Geoffroy Fouquier
  • Jamal Atif
  • Isabelle Bloch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4842)

Abstract

Various kernel functions on graphs have been defined recently. In this article, our purpose is to assess the efficiency of a marginalized kernel for image classification using structural information. Graphs are built from image segmentations, and various types of information concerning the underlying image regions as well as the spatial relationships between them are incorporated as attributes in the graph labeling. The main contribution of this paper consists in studying the impact of fusioning kernels for different attributes on the classification decision, while proposing the use of fuzzy attributes for estimating spatial relationships.

Keywords

Spatial Relation Fuzzy Subset Adjacency Graph Graph Kernel Graph Edit Distance 
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 2007

Authors and Affiliations

  • Emanuel Aldea
    • 1
  • Geoffroy Fouquier
    • 1
  • Jamal Atif
    • 2
  • Isabelle Bloch
    • 1
  1. 1.GET - Télécom Paris (ENST), Dept. TSI, CNRS UMR 5141 LTCI, 46 rue Barrault, 75634 Paris Cedex 13France
  2. 2.Unité ESPACE S140, IRD-Cayenne/UAG, Guyanne Française 

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