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A Nonparametric Classification Algorithm Based on Optimized Templates

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Nonparametric Statistics (ISNPS 2016)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 250))

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Abstract

This contribution is devoted to a classification problem into two groups. A novel algorithm is proposed, which is based on a distance of each observation from the centroid (prototype, template) of one of the groups. The general procedure is described on the particular task of mouth localization in facial images, where the centroid has the form of a mouth template. While templates are most commonly constructed as simple averages of positive examples, the novel optimization criterion allows to improve the separation between observations of one group (images of mouths) and observations of the other group (images of non-mouths). The separation is measured by means of the weighted Pearson product-moment correlation coefficient. On the whole, the new classification method can be described as conceptually simple and at the same time powerful.

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Acknowledgements

This work was financially supported by the Czech Health Research Council project NV15-29835A and the Neuron Fund for Support of Science. The author is thankful to Prof. Dr. Laurie Davies and Dr. Ctirad Matonoha for valuable suggestions.

Author information

Authors and Affiliations

  1. Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic

    J. Kalina

  2. National Institute of Mental Health, Klecany, Czech Republic

    J. Kalina

Authors
  1. J. Kalina
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Correspondence to J. Kalina .

Editor information

Editors and Affiliations

  1. MODAL’X, Paris West University Nanterre La Défense, Nanterre, France

    Patrice Bertail

  2. LMA, Avignon University, Avignon, France

    Delphine Blanke

  3. MIASHS, University of Rennes 2, Rennes, France

    Pierre-André Cornillon

  4. Formation Continue CEPE, Ecole Nationale de la Statistique et de l’Administration, Malakoff, France

    Eric Matzner-Løber

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Kalina, J. (2018). A Nonparametric Classification Algorithm Based on Optimized Templates. In: Bertail, P., Blanke, D., Cornillon, PA., Matzner-Løber, E. (eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics & Statistics, vol 250. Springer, Cham. https://doi.org/10.1007/978-3-319-96941-1_8

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