On the Geodesic Distance in Shapes K-means Clustering

On the Geodesic Distance in Shapes K-means Clustering

In this paper, the problem of clustering rotationally invariant shapes is studied and a solution using Information Geometry tools is provided. Landmarks of a complex shape are defined as probability densities in a statistical manifold. Then, in the setting of shapes clustering through a K-means algo...

Full description

Bibliographic Details
Main Authors: Stefano Antonio Gattone, Angela De Sanctis, Stéphane Puechmorel, Florence Nicol
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Entropy
Subjects:
Shape Analysis
clustering
K-means algorithm
Fisher-Rao metric
wasserstein distance
Online Access:http://www.mdpi.com/1099-4300/20/9/647

Internet

http://www.mdpi.com/1099-4300/20/9/647

Similar Items