Feature Space Reduction for Graph-Based Image Classification

  • Niusvel Acosta-Mendoza
  • Andrés Gago-Alonso
  • Jesús Ariel Carrasco-Ochoa
  • José Francisco Martínez-Trinidad
  • José E. Medina-Pagola
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8258)

Abstract

Feature selection is an essential preprocessing step for classifiers with high dimensional training sets. In pattern recognition, feature selection improves the performance of classification by reducing the feature space but preserving the classification capabilities of the original feature space. Image classification using frequent approximate subgraph mining (FASM) is an example where the benefits of features selections are needed. This is due using frequent approximate subgraphs (FAS) leads to high dimensional representations. In this paper, we explore the use of feature selection algorithms in order to reduce the representation of an image collection represented through FASs. In our results we report a dimensionality reduction of over 50% of the original features and we get similar classification results than those reported by using all the features.

Keywords

Approximate graph mining approximate graph matching feature selection graph-based classification 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Niusvel Acosta-Mendoza
    • 1
    • 2
  • Andrés Gago-Alonso
    • 1
  • Jesús Ariel Carrasco-Ochoa
    • 2
  • José Francisco Martínez-Trinidad
    • 2
  • José E. Medina-Pagola
    • 1
  1. 1.Advanced Technologies Application Center (CENATAV)HavanaCuba
  2. 2.National Institute of Astrophysics, Optics and Electronics (INAOE)PueblaMexico

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