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

Topics: Computational Geometry; Computer Graphics Applications; Computer Vision Applications; Crops and Soils Research; Decision Support Systems; Ecological and Environmental Management; Geocomputation; Geospatial Information and Technologies ; Human Computer Interaction and Visualization; Image Processing and Pattern Recognition; Spatial Analysis and Integration; Spatial Information and Society; Standardization and Interoperability; Urban and Regional Planning

Authors: Raphaël Ceré and François Bavaud

Affiliation: University of Lausanne, Switzerland

ISBN: 978-989-758-252-3

Keyword(s): Free Energy, Image Segmentation, Iterative Clustering, K-means, Laplacian, Modularity, Multivariate Features, Ncut, Soft Membership, Spatial Autocorrelation, Spatial Clustering.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computational Geometry ; Computer Vision, Visualization and Computer Graphics ; Computing and Telecommunications in Cardiology ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Health Engineering and Technology Applications ; Image Formation and Preprocessing ; Knowledge-Based Systems ; Symbolic Systems

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). (More)

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Paper citation in several formats:
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

@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},
}

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

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