Soft Image Segmentation: On the Clustering of Irregular, Weighted, Multivariate Marked Networks

Détails

Ressource 1Télécharger: Cere_Bavaud_SoftImageSegm_2019.pdf (615.32 [Ko])
Etat: Public
Version: Author's accepted manuscript
ID Serval
serval:BIB_2BA2239595E3
Type
Partie de livre
Sous-type
Chapitre: chapitre ou section
Collection
Publications
Titre
Soft Image Segmentation: On the Clustering of Irregular, Weighted, Multivariate Marked Networks
Titre du livre
Communications in Computer and Information Science
Auteur(s)
Ceré Raphaël, Bavaud François
Editeur
Springer International Publishing
ISBN
9783030060091
9783030060107
ISSN
1865-0929
1865-0937
Statut éditorial
Publié
Date de publication
2019
Peer-reviewed
Oui
Editeur scientifique
Ragia   L., Laurini R., Rocha  J.
Série
CCIS vol 936
Genre
Geographical Information Systems Theory, Applications and Management
Pages
85-109
Langue
anglais
Notes
(revised Selected Papers of GISTAM 2017)
Résumé
The contribution exposes and illustrates a general, flexible formalism, together with an associated iterative procedure, aimed at determining soft memberships of marked nodes in a weighted network. Gathering together spatial entities which are both spatially close and similar regarding their features is an issue relevant in image segmentation, spatial clustering, and data analysis in general. Unoriented weighted networks are specified by an ``exchange matrix", determining the probability to select a pair of neighbors. We present a family of membership-dependent free energies, whose local minimization specifies soft clusterings. The free energy additively combines a mutual information, as well as various energy terms, concave or convex in the memberships: within-group inertia, generalized cuts (extending weighted Ncut and modularity), and membership discontinuities (generalizing Dirichlet forms). The framework is closely related to discrete Markov models, random walks, label propagation and spatial autocorrelation (Moran's I), and can express the Mumford-Shah approach. Four small datasets illustrate the theory.
Mots-clé
free energy, image segmentation, iterative clustering, soft K-means, Laplacian, modularity, Moran's I, Mumford-Shah functional, multivariate features, Ncut, soft membership, spatial autocorrelation, spatial clustering
Open Access
Oui
Création de la notice
28/01/2019 20:13
Dernière modification de la notice
20/08/2019 14:11
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