Statistical inference of sensor-based measurements is intensively studied in pattern recognition. It is usually based on feature representations of the objects to be recognized. Such representations, however, neglect the object structure. Structural pattern recognition, on the contrary, focusses on encoding the object structure. As general procedures are still weakly developed, such object descriptions are often application dependent. This hampers the usage of a general learning approach.

This paper aims to summarize the problems and possibilities of general structural inference approaches for the family of sensor-based measurements: images, spectra and time signals, assuming a continuity between measurement samples. In particular it will be discussed when probabilistic assumptions are needed, leading to a statistically-based inference of the structure, and when a pure, non-probabilistic structural inference scheme may be possible.


Feature Representation Dissimilarity Measure Object Structure Proximity Measure Structural Risk Minimization 
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 2006

Authors and Affiliations

  • Robert P. W. Duin
    • 1
  • Elżbieta Pękalska
    • 1
    • 2
  1. 1.ICT group, Faculty of Electr. Eng., Mathematics and Computer ScienceDelft University of TechnologyThe Netherlands
  2. 2.School of Computer ScienceUniversity of ManchesterUnited Kingdom

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