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On the geodesic distance in shapes K-means clustering

Abstract : Using Information Geometry tools, we represent landmarks of a complex shape as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algorithm, we evaluate the discriminative power of two different shapes distances. The first, derived from Fisher-Rao metric, is related with the minimization of information in the Fisher sense and the other is derived from the Wasserstein distance which measures the minimal transportation cost.
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https://hal-enac.archives-ouvertes.fr/hal-01852144
Contributor : Florence Nicol <>
Submitted on : Tuesday, July 31, 2018 - 8:05:44 PM
Last modification on : Tuesday, October 20, 2020 - 10:32:06 AM
Long-term archiving on: : Thursday, November 1, 2018 - 2:22:47 PM

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  • HAL Id : hal-01852144, version 1

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Stefano Gattone, Angela de Sanctis, Stéphane Puechmorel, Florence Nicol. On the geodesic distance in shapes K-means clustering. 2018. ⟨hal-01852144v1⟩

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