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

  • Raphaël CeréEmail author
  • François Bavaud
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 936)

Abstract

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.

Keywords

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 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Geography and SustainabilityUniversity of LausanneLausanneSwitzerland

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