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Feature Selection for Paintings Classification by Optimal Tree Pruning

  • Ana Ioana Deac
  • Jan van der Lubbe
  • Eric Backer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

Abstract

In assessing the authenticity of art work it is of high importance from the art expert point of view to understand the reasoning behind it. While complex data mining tools accompanied by large feature sets extracted from the images can bring accuracy in paintings authentication, it is very difficult or not possible to understand their underlying logic. A small feature set linked to a minor classification error seems to be the key to understanding and interpreting the obtained results. In this study the selection of a small feature set for painting classification is done by the means of building an optimal pruned decision tree. The classification accuracy and the possibility of extracting knowledge for this method are analyzed. The results show that a simple small interpretable feature set can be selected by building an optimal pruned decision tree.

Keywords

Decision Tree Feature Selection Information Gain Feature Selection Method Brush Stroke 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ana Ioana Deac
    • 1
  • Jan van der Lubbe
    • 1
  • Eric Backer
    • 1
  1. 1.Information Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer ScienceUniversity of Technology DelftDelftThe Netherlands

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