Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

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.
Complete list of metadata

Cited literature [22 references]  Display  Hide  Download
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


Files produced by the author(s)


  • HAL Id : hal-01852144, version 1


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



Record views


Files downloads