Multi-labelled Image Segmentation in Irregular, Weighted Networks: A Spatial Autocorrelation Approach

Raphaël Ceré, François Bavaud

Abstract

Image segmentation and spatial clustering both face the same primary problem, namely to gather together spatial entities which are both spatially close and similar regarding their features. The parallelism is particularly obvious in the case of irregular, weighted networks, where methods borrowed from spatial analysis and general data analysis (soft K-means) may serve at segmenting images, as illustrated on four examples. Our approach considers soft memberships (fuzzy clustering) and attempts to minimize a free energy functional made of three ingredients : a within-cluster features dispersion (hard K-means), a network partitioning objective (such as the Ncut or the modularity) and a regularizing entropic term, enabling an iterative computation of the locally optimal soft clusters. In particular, the second functional enjoys many possible formulations, arguably helpful in unifying various conceptualizations of space through the probabilistic selection of pairs of neighbours, as well as their relation to spatial autocorrelation (Moran's I).

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Paper Citation


in Harvard Style

Ceré R. and Bavaud F. (2017). Multi-labelled Image Segmentation in Irregular, Weighted Networks: A Spatial Autocorrelation Approach . In Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM, ISBN 978-989-758-252-3, pages 62-69. DOI: 10.5220/0006322800620069


in Bibtex Style

@conference{gistam17,
author={Raphaël Ceré and François Bavaud},
title={Multi-labelled Image Segmentation in Irregular, Weighted Networks: A Spatial Autocorrelation Approach},
booktitle={Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,},
year={2017},
pages={62-69},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006322800620069},
isbn={978-989-758-252-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - Multi-labelled Image Segmentation in Irregular, Weighted Networks: A Spatial Autocorrelation Approach
SN - 978-989-758-252-3
AU - Ceré R.
AU - Bavaud F.
PY - 2017
SP - 62
EP - 69
DO - 10.5220/0006322800620069