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Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model

Spectral clustering methods have been shown to be effective for image segmentation. Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance. To accommodate for image noise and textural characteristics, this...

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Detalles Bibliográficos
Autores principales: Eichel, Justin A., Wong, Alexander, Fieguth, Paul, Clausi, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873262/
https://www.ncbi.nlm.nih.gov/pubmed/24386111
http://dx.doi.org/10.1371/journal.pone.0082722
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author Eichel, Justin A.
Wong, Alexander
Fieguth, Paul
Clausi, David A.
author_facet Eichel, Justin A.
Wong, Alexander
Fieguth, Paul
Clausi, David A.
author_sort Eichel, Justin A.
collection PubMed
description Spectral clustering methods have been shown to be effective for image segmentation. Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance. To accommodate for image noise and textural characteristics, this study introduces the concept of sub-graph affinity, where each node in the primary graph is modeled as a sub-graph characterizing the neighborhood surrounding the node. The statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty associated with the image noise and textural characteristics by utilizing more information than traditional spectral clustering methods. Experiments using both synthetic and natural images under various levels of noise contamination demonstrate that the proposed approach can achieve improved segmentation performance when compared to existing spectral clustering methods.
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spelling pubmed-38732622014-01-02 Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model Eichel, Justin A. Wong, Alexander Fieguth, Paul Clausi, David A. PLoS One Research Article Spectral clustering methods have been shown to be effective for image segmentation. Unfortunately, the presence of image noise as well as textural characteristics can have a significant negative effect on the segmentation performance. To accommodate for image noise and textural characteristics, this study introduces the concept of sub-graph affinity, where each node in the primary graph is modeled as a sub-graph characterizing the neighborhood surrounding the node. The statistical sub-graph affinity matrix is then constructed based on the statistical relationships between sub-graphs of connected nodes in the primary graph, thus counteracting the uncertainty associated with the image noise and textural characteristics by utilizing more information than traditional spectral clustering methods. Experiments using both synthetic and natural images under various levels of noise contamination demonstrate that the proposed approach can achieve improved segmentation performance when compared to existing spectral clustering methods. Public Library of Science 2013-12-26 /pmc/articles/PMC3873262/ /pubmed/24386111 http://dx.doi.org/10.1371/journal.pone.0082722 Text en © 2013 Eichel et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Eichel, Justin A.
Wong, Alexander
Fieguth, Paul
Clausi, David A.
Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title_full Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title_fullStr Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title_full_unstemmed Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title_short Robust Spectral Clustering Using Statistical Sub-Graph Affinity Model
title_sort robust spectral clustering using statistical sub-graph affinity model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3873262/
https://www.ncbi.nlm.nih.gov/pubmed/24386111
http://dx.doi.org/10.1371/journal.pone.0082722
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