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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Bibliographic Details
Main Authors: Francky Randrianasoa, Jimmy, Kurtz, Camille, Desjardin, Eric, Passat, Nicolas
Other Authors: 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
Language:English
Published: HAL CCSD 2015
Subjects:
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]
Iceland
Online Access: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
Description
Summary: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.

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