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 metadatas

https://hal-enac.archives-ouvertes.fr/hal-01852144
Contributor : Laurence Porte <>
Submitted on : Thursday, October 4, 2018 - 11:11:28 AM
Last modification on : Monday, December 10, 2018 - 4:14:08 PM
Long-term archiving on : Saturday, January 5, 2019 - 2:34:15 PM

File

entropy_20_00647_manuscript_v2...
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Stefano Gattone, Angela de Sanctis, Stéphane Puechmorel, Florence Nicol. On the geodesic distance in shapes K-means clustering. Entropy, MDPI, 2018, Special Issue Selected Papers from 4th International Electronic Conference on Entropy and Its Applications, 20 (9), pp 647. ⟨10.3390/e20090647⟩. ⟨hal-01852144v2⟩

Share

Metrics

Record views

40

Files downloads

104