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.
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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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Iwata, K. (2018). Shape Clustering as a Type of Procrustes Analysis. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11304. Springer, Cham. https://doi.org/10.1007/978-3-030-04212-7_19
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DOI: https://doi.org/10.1007/978-3-030-04212-7_19
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