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Journal articles
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.
Cited literature [1 references]
https://hal-enac.archives-ouvertes.fr/hal-01852144
Contributor : Laurence Porte <>
Submitted on : Thursday, October 4, 2018 - 11:11:28 AM
Last modification on : Tuesday, October 20, 2020 - 10:32:06 AM
Long-term archiving on: : Saturday, January 5, 2019 - 2:34:15 PM
Contributor : Laurence Porte <>
Submitted on : Thursday, October 4, 2018 - 11:11:28 AM
Last modification on : Tuesday, October 20, 2020 - 10:32:06 AM
Long-term archiving on: : Saturday, January 5, 2019 - 2:34:15 PM
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