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k-Morik: Mining Patterns to Classify Cartified Images of Katharina

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Solving Large Scale Learning Tasks. Challenges and Algorithms

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9580))

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Abstract

When building traditional Bag of Visual Words (BOW) for image classification, the k-Means algorithm is usually used on a large set of high dimensional local descriptors to build a visual dictionary. However, it is very likely that, to find a good visual vocabulary, only a sub-part of the descriptor space of each visual word is truly relevant for a given classification problem. In this paper, we explore a novel framework for creating a visual dictionary based on Cartification and Pattern Mining instead of the traditional k-Means algorithm. Preliminary experimental results on face images show that our method is able to successfully differentiate photos of Elisa Fromont, and Bart Goethals from Katharina Morik.

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Author information

Authors and Affiliations

  1. UMR CNRS 5516, Laboratoire Hubert-Curien, Université de Lyon, Université de St-Etienne, 42000, St-Etienne, France

    Elisa Fromont & Bart Goethals

  2. Department of Math and Computer Science, University of Antwerp, Antwerp, Belgium

    Bart Goethals

Authors
  1. Elisa Fromont
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  2. Bart Goethals
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Corresponding author

Correspondence to Bart Goethals .

Editor information

Editors and Affiliations

  1. TU Dortmund , Dortmund, Germany

    Stefan Michaelis

  2. TU Dortmund , Dortmund, Germany

    Nico Piatkowski

  3. TU Dortmund , Dortmund, Germany

    Marco Stolpe

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Fromont, E., Goethals, B. (2016). k-Morik: Mining Patterns to Classify Cartified Images of Katharina. In: Michaelis, S., Piatkowski, N., Stolpe, M. (eds) Solving Large Scale Learning Tasks. Challenges and Algorithms. Lecture Notes in Computer Science(), vol 9580. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_21

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  • DOI: https://doi.org/10.1007/978-3-319-41706-6_21

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  • Online ISBN: 978-3-319-41706-6

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