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2018 | OriginalPaper | Chapter

Directional Data Analysis for Shape Classification

Authors : Adrián Muñoz, Alberto Suárez

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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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.

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Metadata
Title
Directional Data Analysis for Shape Classification
Authors
Adrián Muñoz
Alberto Suárez
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01417-9

Electronic ISBN: 978-3-030-01418-6

Copyright Year: 2018

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

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