Multi-image Segmentation: A Collaborative Approach Based on Binary Partition Trees

  • Jimmy Francky Randrianasoa
  • Camille Kurtz
  • Éric Desjardin
  • Nicolas Passat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9082)


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.


Segmentation fusion Morphological hierarchies Multi-image Collaborative strategies Binary partition tree Remote sensing 


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jimmy Francky Randrianasoa
    • 1
  • Camille Kurtz
    • 2
  • Éric Desjardin
    • 1
  • Nicolas Passat
    • 1
  1. 1.CReSTICUniversité de Reims Champagne-ArdenneReimsFrance
  2. 2.LIPADEUniversité Paris-DescartesParisFrance

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