Skip to main content
SpringerLink
Log in

Efficient tree construction for multiscale image representation and processing

  • Original Research Paper
  • Published:
Journal of Real-Time Image Processing Aims and scope Submit manuscript

Abstract

With the continuous growth of sensor performances, image analysis and processing algorithms have to cope with larger and larger data volumes. Besides, the informative components of an image might not be the pixels themselves, but rather the objects they belong to. This has led to a wide range of successful multiscale techniques in image analysis and computer vision. Hierarchical representations are thus of first importance, and require efficient algorithms to be computed in order to address real-life applications. Among these hierarchical models, we focus on morphological trees (e.g., min/max-tree, tree of shape, binary partition tree, α-tree) that come with interesting properties and already led to appropriate techniques for image processing and analysis, with a growing interest from the image processing community. More precisely, we build upon two recent algorithms for efficient α-tree computation and introduce several improvements to achieve higher performance. We also discuss the impact of the data structure underlying the tree representation, and provide for the sake of illustration several applications where efficient multiscale image representation leads to fast but accurate techniques, e.g., in remote sensing image analysis or video segmentation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. https://github.com/jirihavel/libcct.

References

  1. Alonso-Gonzalez, A., Valero, S., Chanussot, J., Lopez-Martinez, C., Salembier, P.: Processing multidimensional sar and hyperspectral images with binary partition tree. Proc. IEEE 101(3), 723–747 (2013)

    Article  Google Scholar 

  2. Bai, X., Wang, J., Simons, D., Sapiro, G.: Video snapcut: robust video object cutout using localized classifiers. In: Proceedings of the SIGGRAPH (2009)

  3. Bosilj, P., Lefèvre, S., Kijak, E.: Hierarchical image representation simplification driven by region complexity. In: International Conference on Image Analysis and Processing, pp. 562–571. (2013)

  4. Boykov, Y., Funka-Lea, G.: Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)

    Article  Google Scholar 

  5. Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proceedings of the ICCV, pp. 105–112 (2001)

  6. Brunner, D., Soille, P.: Iterative area filtering of multichannel images. Image Vis. Comput. 25(8), 1352–1364 (2007)

    Article  Google Scholar 

  7. Carlinet, E., Géraud, T.: A comparison of many max-tree computation algorithms. In: Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 73–85. Springer (2013)

  8. Carlinet, E., Géraud, T.: A comparative review of component tree computation algorithms. IEEE Trans. Image Process. 23(9), 3885–3895 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  9. Cousty, J., Najman, L., Perret, B.: Constructive links between some morphological hierarchies on edge-weighted graphs. In: International Symposium on Mathematical, Morphology, pp. 135–146 (2013)

  10. Cramer, M.: The dgpf test on digital aerial camera evaluation—overview and test design. Photogrammetrie Fernerkundung Geoinf. 2, 73–82 (2010)

    Article  Google Scholar 

  11. Grundmann, M., Kwatra, V., Han, M., Essa, I.: Efficient hierarchical graph based video segmentation. IEEE CVPR (2010)

  12. Havel, J., Merciol, F., Lefèvre, S.: Efficient schemes for computing \(\alpha\)-tree representations. In: International Syposium on Mathematical, Morphology, pp. 111–122 (2013)

  13. Jegou, H., Douze, M., Schmid, C.: Hamming embedding and weak geometry consistency for large scale image search. In: Proceedings of the 10th European conference on Computer vision (2008)

  14. Lee, Y.J., Kim, J., Grauman, K.: Key-segments for video object segmentation. In: ICCV (2011)

  15. Lefèvre, S., Chapel, L., Merciol, F.: Hyperspectral image classification from multiscale description with constrained connectivity and metric learning. In: 6th International Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) (2014)

  16. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings of the 8th International Conference on Computer Vision, vo. 2, pp. 416-425. Vancouver (2001)

  17. Matas, P., Dokladalova, E., Akil, M., Georgiev, V., Poupa, M.: Parallel hardware implementation of connected component tree computation. In: 2010 International Conference on Field Programmable Logic and Applications (FPL), pp. 64 –69 (2010)

  18. Merciol, F., Chapel, L., Lefèvre, S.: Hyperspectral image representation through \(\alpha\)-trees. In: ESA-EUSC-JRC Conference on Image Information Mining, pp. 37–40 (2014)

  19. Merciol, F., Lefèvre, S.: Fast image and video segmentation based on \(\alpha\)-tree multiscale representation. In: International Conference on Signal Image Technology and Internet Based Systems, pp. 336–342. Naples (2012)

  20. Monasse, P.: Contrast invariant registration of images. Proc. Int. Conf. Acoust. Speech Signal Process. 6, 3221–3224 (1999)

    Google Scholar 

  21. Monasse, P., Guichard, F.: Fast computation of a contrast-invariant image representation. IEEE Trans. Image Process. 9(5), 860–872 (2000)

    Article  Google Scholar 

  22. Najman, L., Couprie, M.: Building the component tree in quasi-linear time. IEEE Trans. Image Process. 15(11), 3531–3539 (2006)

    Article  Google Scholar 

  23. Najman, L., Cousty, J., Perret, B.: Playing with kruskal: algorithms for morphological trees in edgeweightted graphs. In: International Syposium on Mathematical Morphology, pp. 135-146 (2013)

  24. Nister, D., Stewenius, H.: Linear time maximally stable extremal regions. In: ECCV, pp. 183–196 (2008)

  25. Noma, A., Graciano, A., Cesar Jr., R., Consularo, L., Bloch, I.: Interactive image segmentation by matching attributed relational graphs. Pattern Recogn. 45, 1159–1179 (2012)

    Article  Google Scholar 

  26. Ouzounis, G., Gueguen, L.: Interactive collection of training samples from the max-tree structure. In: IEEE International Conference on Image Processing, pp. 1449–1452 (2011)

  27. Ouzounis, G., Syrris, V., Gueguen, L., Soille, P.: The switchboard platform for interactive image information mining. In: Soille, P., Iapaolo, M., Marchetti, P.G., Datcu, M. (eds.) Proceedings of 8th Conference on Image Information Mining, pp. 26–30. ESA-EUSC-JRC, Munich (2012)

    Google Scholar 

  28. Ouzounis, G., Wilkinson, M.: Mask-based second-generation connectivity and attribute filters. IEEE Trans. Pattern Anal. Mach. Intel. 29(6), 990–1004 (2007)

    Article  Google Scholar 

  29. Ouzounis, G.K., Soille, P.: Pattern spectra from partition pyramids and hierarchies. In: International Symposium on Mathematical Morphology, pp. 108–119, Verbania-Intra (2011)

  30. Passat, N., Naegel, B.: Selection of relevant nodes from component-trees in linear time. In: IAPR International Conference on Discrete Geometry for Computer Imagery, pp. 453–464 (2011)

  31. Passat, N., Naegel, N., Rousseau, F., Koob, M., Dietemann, J.L.: Interactive segmentation based on component-trees. Pattern Recogn. 44(10–11), 2539–2554 (2011)

    Article  MATH  Google Scholar 

  32. Poullot, S., Satoh, S.: Vabcut: a video extension of grabcut for unsupervised video foreground object segmentation. In: VISAPP (2014)

  33. Price, B., Morse, B., Cohen, S.: Livecut: Learning-based interactive video segmentation by evaluation of multiple propagated cues. In: IEEE International Conference on Computer Vision (2009)

  34. Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. Image Process. 9(4), 561–576 (2000)

    Article  Google Scholar 

  35. Salembier, P., Oliveras, A., Garrido, L.: Anti-extensive connected operators for image and sequence processing. IEEE Trans. Image Process. 7(4), 555–570 (1998)

    Article  Google Scholar 

  36. Serra, J.: Anamorphoses and function lattices. In: Dougherty, E.R. (ed.) Mathematical Morphology in Image Processing, pp. 483–523. Marcel Dekker, New York (1993)

    Google Scholar 

  37. Serra, J.: The “false colour” problem. In: International Symposium on Mathematical Morphology, pp. 13–23. Groningen (2009)

  38. Serra, J., Kiran, B.: Optima on hierarchies of partitions. In: International Symposium on Mathematical, Morphology, pp. 147–158 (2013)

  39. Soille, P.: Constrained connectivity for hierarchical image partitioning and simplification. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1132–1145 (2008)

    Article  Google Scholar 

  40. Soille, P., Grazzini, J.: Constrained connectivity and transition regions. In: International Symposium on Mathematical Morphology, pp. 59–69. Groningen (2009)

  41. Song, Y., Zhang, A.: Analyzing scenery images by monotonic tree. ACM Multimed. Syst. 8(6), 495–511 (2003)

    Article  Google Scholar 

  42. Crozet, S., Géraud, T., Carlinet, E., Najman, L.: A quasi-linear algorithm to compute the tree of shapes of nd images. In: Mathematical Morphology and Its Applications to Signal and Image Processing, pp. 98–110. Springer (2013)

  43. Tarjan, R.E.: Efficiency of a good but not linear set union algorithm. J. ACM 22, 215–225 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  44. Tsai, D., Flagg, M., Rehg, J.M.: Motion coherent tracking with multi-label mrf optimization. British Machine Vision Conference (2010)

  45. Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Trans. Image Process. 22(4), 1430–1443 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  46. Vilaplana, V., Marques, F., Salembier, P.: Binary partition trees for object detection. IEEE Trans. Image Process. 17(11), 2201–2216 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  47. Wang, J., Bhat, P., Colburn, R.A., Agrawala, M., Cohen, M.F.: Interactive video cutout. ACM Trans. Gr. 24(3), 585–594 (2005)

    Article  Google Scholar 

  48. Wang, T., Han, B., Collomosse, J.: Touchcut: fast image and video segmentation using single-touch interaction. Comput. Vis. Image Underst. 120, 14–30 (2014)

    Article  Google Scholar 

  49. Weber, J., Lefèvre, S., Gançarski, P.: Interactive video segmentation based on quasi-flat zones. In: International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 265–270. Dubrovnik (2011)

  50. Michael, H.F., Wilkinson, H.G., Wim, H.H., Jan-Eppo, J., Arnold, M.: Concurrent computation of attribute filters on shared memory parallel machines. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1800–1813 (2008)

    Article  Google Scholar 

  51. Xu, C., Corso, J.J.: Evaluation of super-voxel methods for early video processing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2012)

Download references

Acknowledgments

The authors would like to thank Michael Wilkinson for fruitful discussions and valuable suggestions, which helped in improving the manuscript. The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [10].

Author information

Authors and Affiliations

  1. Brno University of Technology, Brno, Czech Republic

    Jiří Havel

  2. Université de Bretagne-Sud, IRISA, Vannes, France

    François Merciol & Sébastien Lefèvre

Authors
  1. Jiří Havel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. François Merciol
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sébastien Lefèvre
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiří Havel.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Havel, J., Merciol, F. & Lefèvre, S. Efficient tree construction for multiscale image representation and processing. J Real-Time Image Proc 16, 1129–1146 (2019). https://doi.org/10.1007/s11554-016-0604-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11554-016-0604-0

Keywords

Search

Navigation