Multi-image segmentation: A collaborative approach based on binary partition trees
International audience Image segmentation is generally performed in a "one image, one algorithm" paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant informa...
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ftccsdartic:oai:HAL:hal-01695074v1 2023-05-15T16:50:43+02:00 Multi-image segmentation: A collaborative approach based on binary partition trees Francky Randrianasoa, Jimmy Kurtz, Camille Desjardin, Eric Passat, Nicolas Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC) Université de Reims Champagne-Ardenne (URCA) Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517) Université Paris Descartes - Paris 5 (UPD5) ANR-10-BLAN-0205,KIDICO,Intégration des connaissances pour la convolution discrète, la segmentation et la reconstruction d'informations dans les images digitales(2010) ANR-12-MONU-0001,Coclico,COllaboration, CLassification, Incrémentalité et COnnaissances(2012) Reykjavik, Iceland 2015 https://hal.univ-reims.fr/hal-01695074 https://hal.univ-reims.fr/hal-01695074/document https://hal.univ-reims.fr/hal-01695074/file/Randrianasoa_ISMM_2015.pdf https://doi.org/10.1007/978-3-319-18720-4_22 en eng HAL CCSD Springer info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-18720-4_22 hal-01695074 https://hal.univ-reims.fr/hal-01695074 https://hal.univ-reims.fr/hal-01695074/document https://hal.univ-reims.fr/hal-01695074/file/Randrianasoa_ISMM_2015.pdf doi:10.1007/978-3-319-18720-4_22 info:eu-repo/semantics/OpenAccess Lecture Notes in Computer Science International Symposium on Mathematical Morphology (ISMM) https://hal.univ-reims.fr/hal-01695074 International Symposium on Mathematical Morphology (ISMM), 2015, Reykjavik, Iceland. pp.253-264, ⟨10.1007/978-3-319-18720-4_22⟩ morphological hierarchies multi-image collaborative strategies binary partition tree remote sensing segmentation fusion col- laborative strategies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] info:eu-repo/semantics/conferenceObject Conference papers 2015 ftccsdartic https://doi.org/10.1007/978-3-319-18720-4_22 2021-10-31T00:59:45Z International audience Image segmentation is generally performed in a "one image, one algorithm" paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the "n images" part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing. Conference Object Iceland Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) 253 264 |
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Open Polar |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
English |
topic |
morphological hierarchies multi-image collaborative strategies binary partition tree remote sensing segmentation fusion col- laborative strategies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
spellingShingle |
morphological hierarchies multi-image collaborative strategies binary partition tree remote sensing segmentation fusion col- laborative strategies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Francky Randrianasoa, Jimmy Kurtz, Camille Desjardin, Eric Passat, Nicolas Multi-image segmentation: A collaborative approach based on binary partition trees |
topic_facet |
morphological hierarchies multi-image collaborative strategies binary partition tree remote sensing segmentation fusion col- laborative strategies [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] |
description |
International audience Image segmentation is generally performed in a "one image, one algorithm" paradigm. However, it is sometimes required to consider several images of a same scene, or to carry out several (or several occurrences of a same) algorithm(s) to fully capture relevant information. To solve the induced segmentation fusion issues, various strategies have been already investigated for allowing a consensus between several segmentation outputs. This article proposes a contribution to segmentation fusion, with a specific focus on the "n images" part of the paradigm. Its main originality is to act on the segmentation research space, i.e., to work at an earlier stage than standard segmentation fusion approaches. To this end, an algorithmic framework is developed to build a binary partition tree in a collaborative fashion, from several images, thus allowing to obtain a unified hierarchical segmentation space. This framework is, in particular, designed to embed consensus policies inherited from the machine learning domain. Application examples proposed in remote sensing emphasise the potential usefulness of our approach for satellite image processing. |
author2 |
Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC) Université de Reims Champagne-Ardenne (URCA) Laboratoire d'Informatique Paris Descartes (LIPADE - EA 2517) Université Paris Descartes - Paris 5 (UPD5) ANR-10-BLAN-0205,KIDICO,Intégration des connaissances pour la convolution discrète, la segmentation et la reconstruction d'informations dans les images digitales(2010) ANR-12-MONU-0001,Coclico,COllaboration, CLassification, Incrémentalité et COnnaissances(2012) |
format |
Conference Object |
author |
Francky Randrianasoa, Jimmy Kurtz, Camille Desjardin, Eric Passat, Nicolas |
author_facet |
Francky Randrianasoa, Jimmy Kurtz, Camille Desjardin, Eric Passat, Nicolas |
author_sort |
Francky Randrianasoa, Jimmy |
title |
Multi-image segmentation: A collaborative approach based on binary partition trees |
title_short |
Multi-image segmentation: A collaborative approach based on binary partition trees |
title_full |
Multi-image segmentation: A collaborative approach based on binary partition trees |
title_fullStr |
Multi-image segmentation: A collaborative approach based on binary partition trees |
title_full_unstemmed |
Multi-image segmentation: A collaborative approach based on binary partition trees |
title_sort |
multi-image segmentation: a collaborative approach based on binary partition trees |
publisher |
HAL CCSD |
publishDate |
2015 |
url |
https://hal.univ-reims.fr/hal-01695074 https://hal.univ-reims.fr/hal-01695074/document https://hal.univ-reims.fr/hal-01695074/file/Randrianasoa_ISMM_2015.pdf https://doi.org/10.1007/978-3-319-18720-4_22 |
op_coverage |
Reykjavik, Iceland |
genre |
Iceland |
genre_facet |
Iceland |
op_source |
Lecture Notes in Computer Science International Symposium on Mathematical Morphology (ISMM) https://hal.univ-reims.fr/hal-01695074 International Symposium on Mathematical Morphology (ISMM), 2015, Reykjavik, Iceland. pp.253-264, ⟨10.1007/978-3-319-18720-4_22⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-319-18720-4_22 hal-01695074 https://hal.univ-reims.fr/hal-01695074 https://hal.univ-reims.fr/hal-01695074/document https://hal.univ-reims.fr/hal-01695074/file/Randrianasoa_ISMM_2015.pdf doi:10.1007/978-3-319-18720-4_22 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1007/978-3-319-18720-4_22 |
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253 |
op_container_end_page |
264 |
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1766040841583853568 |