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GriMa: A Grid Mining Algorithm for Bag-of-Grid-Based Classification

  • Romain Deville
  • Elisa FromontEmail author
  • Baptiste Jeudy
  • Christine Solnon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

General-purpose exhaustive graph mining algorithms have seldom been used in real life contexts due to the high complexity of the process that is mostly based on costly isomorphism tests and countless expansion possibilities. In this paper, we explain how to exploit grid-based representations of problems to efficiently extract frequent grid subgraphs and create Bag-of-Grids which can be used as new features for classification purposes. We provide an efficient grid mining algorithm called GriMA which is designed to scale to large amount of data. We apply our algorithm on image classification problems where typical Bag-of-Visual-Words-based techniques are used. However, those techniques make use of limited spatial information in the image which could be beneficial to obtain more discriminative features. Experiments on different datasets show that our algorithm is efficient and that adding the structure may greatly help the image classification process.

Keywords

Support Vector Machine Visual Word Frequent Pattern Image Classification Mining Algorithm 
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.

Notes

Acknowledgement

This work has been supported by the ANR project SoLStiCe (ANR-13-BS02-0002-01).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Romain Deville
    • 1
    • 2
  • Elisa Fromont
    • 1
    Email author
  • Baptiste Jeudy
    • 1
  • Christine Solnon
    • 2
  1. 1.Université de Lyon, UJM, Laboratoire Hubert Curien (CNRS, UJM, IOGS) UMR 5516Saint-EtienneFrance
  2. 2.Université de Lyon, INSA-Lyon, LIRIS, UMR5205LyonFrance

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