PropertyValue
?: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. ()
?:bookTitle
  • EGC (best of volume) ()
?:citationCount
  • 2 ()
is ?:cites of
?:cites
?:created
  • 2016-06-24 ()
?:creator
?:doi
  • 10.1007/978-3-642-00580-0_5 ()
?:endingPage
  • 94 ()
?:estimatedCitationCount
  • 2 ()
is ?:hasCitingEntity of
?:hasDiscipline
?:hasURL
?:language
  • en ()
?:publicationDate
  • 2010-01-01 ()
?:publisher
  • Springer, Berlin, Heidelberg ()
?:rank
  • 22139 ()
?:referenceCount
  • 23 ()
?:startingPage
  • 77 ()
?:title
  • Toward a Better Integration of Spatial Relations in Learning with Graphical Models ()
?:type

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