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Shape Clustering as a Type of Procrustes Analysis

  • Kazunori IwataEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11304)

Abstract

The ordinary Procrustes sum of squares is one of the most important measures in Procrustes analysis of shape. In this paper, we incorporate a competitive learning scheme into Procrustes analysis. We introduce a measure of distance between the landmarks of shapes for a competitive learning scheme. Thus, we present novel shape clustering as a type of Procrustes analysis. Using datasets of line drawings and outlines, we show that shape clustering performs well for shape classification compared with shape clustering founded on typical vector-based distances.

Keywords

Shape clustering Procrustes analysis Competitive learning 

Notes

Acknowledgments

We wish to thank Yoshitaka Toda for his support with the experiments. We thank Maxine Garcia, PhD, from Edanz Group (www.edanzediting.com/ac) for editing a draft of this manuscript. This work was supported in part by JSPS KAKENHI Grant Number JP16K00308 and Hiroshima City University TOKUTEI-KENKYUHI Grant Number 1150347.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan

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