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Journal of Mathematical Imaging and Vision

, Volume 60, Issue 4, pp 479–502 | Cite as

Hierarchical Segmentations with Graphs: Quasi-flat Zones, Minimum Spanning Trees, and Saliency Maps

  • Jean Cousty
  • Laurent Najman
  • Yukiko Kenmochi
  • Silvio Guimarães
Article

Abstract

Hierarchies of partitions are generally represented by dendrograms (direct representation). They can also be represented by saliency maps or minimum spanning trees. In this article, we precisely study the links between these three representations. In particular, we provide a new bijection between saliency maps and hierarchies based on quasi-flat zones as often used in image processing and we characterize saliency maps and minimum spanning trees as solutions to constrained minimization problems where the constraint is quasi-flat zones preservation. In practice, these results make up a toolkit for designing new hierarchical methods where one can choose the most convenient representation. They also invite us to process non-image data with morphological hierarchies. More precisely, we show the practical interest of the proposed framework for: (i) hierarchical watershed image segmentations, (ii) combinations of different hierarchical segmentations, (iii) hierarchicalizations of some non-hierarchical image segmentation methods based on regional dissimilarities, and (iv) hierarchical analysis of geographic data.

Keywords

Mathematical morphology Hierarchy of partitions Hierarchical image segmentation Watershed Saliency maps Minimum spanning trees Hierarchical classification 

Notes

Acknowledgements

The research leading to these results has received funding from the French Agence Nationale de la Recherche (Contract ANR-2010-BLAN-0205-03), the French Committee for the Evaluation of Academic and Scientific Cooperation with Brazil, and the Brazilian Federal Agency of Support and Evaluation of Postgraduate Education (Program CAPES/PVE: Grant 064965/2014-01, and Program CAPES/COFECUB: Grant 592/08).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jean Cousty
    • 1
  • Laurent Najman
    • 1
  • Yukiko Kenmochi
    • 1
  • Silvio Guimarães
    • 1
    • 2
  1. 1.Université Paris-Est, Laboratoire d’Informatique Gaspard-Monge, A3SI, ESIEE Paris, CNRSMarne-la-ValléeFrance
  2. 2.PUC Minas - ICEI - DCC - VIPLABBelo HorizonteBrazil

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