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Directional Data Analysis for Shape Classification

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Artificial Neural Networks and Machine Learning – ICANN 2018 (ICANN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11139))

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Abstract

In this work we address the problem of learning from images to perform grouping and classification of shapes. The key idea is to encode the instances available for learning in the form of directional data. In two dimensions, the figure to be categorized is characterized by the distribution of the directions of the normal unit vectors along the contour of the object. This directional characterization is used to extract characteristics based on metrics defined in the space of circular distributions. These characteristics can then be used to categorize the encoded shapes. The usefulness of the representation proposed is illustrated in the problem of clustering and classification of otolith shapes.

The authors acknowledge financial support from the Spanish Ministry of Economy, Industry and Competitiveness, project TIN2016-76406-P.

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Authors and Affiliations

  1. Computer Science Department, Universidad Autónoma de Madrid, C. Francisco Tomás y Valiente 11, 28049, Madrid, Spain

    Adrián Muñoz & Alberto Suárez

Authors
  1. Adrián Muñoz
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  2. Alberto Suárez
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Corresponding author

Correspondence to Adrián Muñoz .

Editor information

Editors and Affiliations

  1. Czech Academy of Sciences, Prague 8, Czech Republic

    Věra Kůrková

  2. Open University of Cyprus, Latsia, Cyprus

    Yannis Manolopoulos

  3. CITEC Bielefeld University, Bielefeld, Germany

    Barbara Hammer

  4. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  5. University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

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Muñoz, A., Suárez, A. (2018). Directional Data Analysis for Shape Classification. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_59

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  • DOI: https://doi.org/10.1007/978-3-030-01418-6_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01417-9

  • Online ISBN: 978-3-030-01418-6

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