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)

Abstract

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.

Keywords

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

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References

  1. 1.
    Topchy, A., Jain, A.K., Punch, W.: Clustering ensembles: Models of consensus and weak partitions. IEEE TPAMI 27, 1866–1881 (2005)CrossRefGoogle Scholar
  2. 2.
    Salembier, P., Wilkinson, M.H.F.: Connected operators: A review of region-based morphological image processing techniques. IEEE SPM 26, 136–157 (2009)CrossRefGoogle Scholar
  3. 3.
    Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE TIP 9, 561–576 (2000)Google Scholar
  4. 4.
    Rohlfing, T., Maurer Jr., C.R.: Shape-based averaging. IEEE TIP 16, 153–161 (2007)MathSciNetGoogle Scholar
  5. 5.
    Vidal, J., Crespo, J., Maojo, V.: A shape interpolation technique based on inclusion relationships and median sets. IVC 25, 1530–1542 (2007)CrossRefGoogle Scholar
  6. 6.
    Franek, L., Abdala, D.D., Vega-Pons, S., Jiang, X.: Image segmentation fusion using general ensemble clustering methods. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part IV. LNCS, vol. 6495, pp. 373–384. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Mignotte, M.: Segmentation by fusion of histogram-based K-means clusters in different color spaces. IEEE TIP 17, 780–787 (2008)MathSciNetGoogle Scholar
  8. 8.
    Calderero, F., Eugenio, F., Marcello, J., Marqués, F.: Multispectral cooperative partition sequence fusion for joint classification and hierarchical segmentation. IEEE GRSL 9, 1012–1016 (2012)Google Scholar
  9. 9.
    Wang, H., Zhang, Y., Nie, R., Yang, Y., Peng, B., Li, T.: Bayesian image segmentation fusion. KBS 71, 162–168 (2014)Google Scholar
  10. 10.
    Chu, C.C., Aggarwal, J.K.: The integration of image segmentation maps using region and edge information. IEEE TPAMI 15, 72–89 (1993)Google Scholar
  11. 11.
    Cho, K., Meer, P.: Image segmentation from consensus information. CVIU 68, 72–89 (1997)Google Scholar
  12. 12.
    Angulo, J., Jeulin, D.: Stochastic watershed segmentation. In: ISMM, pp. 265–276 (2007)Google Scholar
  13. 13.
    Bernard, K., Tarabalka, Y., Angulo, J., Chanussot, J., Benediktsson, J.A.: Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach. IEEE TIP 21, 2008–2021 (2012)MathSciNetGoogle Scholar
  14. 14.
    Wattuya, P., Rothaus, K., Praßni, J.S., Jiang, X.: A random walker based approach to combining multiple segmentations. In: ICPR, pp. 1–4 (2008)Google Scholar
  15. 15.
    USalembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE TIP 7, 555–570 (1998)Google Scholar
  16. 16.
    Monasse, P., Guichard, F.: Scale-space from a level lines tree. JVCIR 11, 224–236 (2000)Google Scholar
  17. 17.
    Soille, P.: Constrained connectivity for hierarchical image decomposition and simplification. IEEE TPAMI 30, 1132–1145 (2008)CrossRefGoogle Scholar
  18. 18.
    Vilaplana, V., Marques, F., Salembier, P.: Binary partition trees for object detection. IEEE TIP 17, 2201–2216 (2008)MathSciNetGoogle Scholar
  19. 19.
    Benediktsson, J.A., Bruzzone, L., Chanussot, J., Dalla Mura, M., Salembier, P., Valero, S.: Hierarchical analysis of remote sensing data: Morphological attribute profiles and binary partition trees. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 306–319. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Kurtz, C., Passat, N., Gançarski, P., Puissant, A.: Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology. PR 45, 685–706 (2012)Google Scholar
  21. 21.
    Akcay, H.G., Aksoy, S.: Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE TGRS 46, 2097–2111 (2008)Google Scholar
  22. 22.
    Kurtz, C., Naegel, B., Passat, N.: Connected filtering based on multivalued component-trees. IEEE TIP 23, 5152–5164 (2014)MathSciNetGoogle Scholar
  23. 23.
    Alonso-González, A., López-Martínez, C., Salembier, P.: PolSAR time series processing with binary partition trees. IEEE TGRS 52, 3553–3567 (2014)Google Scholar

Copyright information

© 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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