GraphBPT: An Efficient Hierarchical Data Structure for Image Representation and Probabilistic Inference

  • Abdullah Al-Dujaili
  • François Merciol
  • Sébastien Lefèvre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9082)

Abstract

This paper presents GraphBPT, a tool for hierarchical representation of images based on binary partition trees. It relies on a new BPT construction algorithm that have interesting tuning properties. Besides, access to image pixels from the tree is achieved efficiently with data compression techniques, and a textual representation of BPT is also provided for interoperability. Finally, we illustrate how the proposed tool takes benefit from probabilistic inference techniques by empowering the BPT with its equivalent factor graph. The relevance of GraphBPT is illustrated in the context of image segmentation.

Keywords

Image processing Hierarchical segmentation Binary partition tree Compression Probabilistic inference 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 80, 91–106 (2013)CrossRefGoogle Scholar
  2. 2.
    Blaschke, T., Lang, S., Lorup, E., Strobl, J., Zeil, P.: Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public 2, 555–570 (2000)Google Scholar
  3. 3.
    Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS Journal of Photogrammetry and Remote Sensing 58(3–4), 225–238 (2004)CrossRefGoogle Scholar
  4. 4.
    Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8), 1915–1929 (2013)CrossRefGoogle Scholar
  5. 5.
    Lefvre, S., Chapel, L., Merciol, F.: Hyperspectral image classification from multiscale description with constrained connectivity and metric learning. In: Proceedings of the 6th International Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014 (2014)Google Scholar
  6. 6.
    Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Transactions on Image Processing 22(4), 1430–1443 (2013)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning. The MIT Press (2009)Google Scholar
  8. 8.
    Salembier, P., Oliveras, A., Garrido, L.: Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing 7(4), 555–570 (1998)CrossRefGoogle Scholar
  9. 9.
    Jones, R.: Component trees for image filtering and segmentation. In: IEEE Workshop on Nonlinear Signal and Image Processing, NSIP (1997)Google Scholar
  10. 10.
    Monasse, P., Guichard, F.: Scale-space from a level lines tree. Journal of Visual Communication and Image Representation 11(2), 224–236 (2000)CrossRefGoogle Scholar
  11. 11.
    Garrido, L., Salembier, P., Garcia, D.: Extensive operators in partition lattices for image sequence analysis. Signal Processing 66(2), 157–180 (1998)CrossRefMATHGoogle Scholar
  12. 12.
    Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Transactions on Image Processing 9(4), 561–576 (2000)CrossRefGoogle Scholar
  13. 13.
    Salerno, O., Pardàs, M., Vilaplana, V., Marqués, F.: Object recognition based on binary partition trees. In: International Conference on Image Processing, vol. 2, pp. 929–932. IEEE (2004)Google Scholar
  14. 14.
    Vilaplana, V., Marques, F., Salembier, P.: Binary partition trees for object detection. IEEE Transactions on Image Processing 17(11), 2201–2216 (2008)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Giró-i Nieto, X.: Part-Based Object Retrieval With Binary Partition Trees. PhD thesis, Universitat Politècnica de Catalunya, UPC (2012)Google Scholar
  16. 16.
    Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Transactions on Image Processing 22(4), 1430–1443 (2013)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Garrido, L.: Hierarchical Region Based Processing of Images and Video Sequences: Application to Filtering, Segmentation and Information Retrieval. PhD thesis, Universitat Politècnica de Catalunya, UPC (2002)Google Scholar
  18. 18.
    Liu, T., Yuan, Z., Sun, J., Wang, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(2), 353–367 (2011)CrossRefGoogle Scholar
  19. 19.
    Bo, L., Lai, K., Ren, X., Fox, D.: Object recognition with hierarchical kernel descriptors. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1729–1736. IEEE (2011)Google Scholar
  20. 20.
    Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision 6(3–4), 185–365 (2011)MATHGoogle Scholar
  21. 21.
    Mooij, J.M.: libDAI: A free and open source C++ library for discrete approximate inference in graphical models. Journal of Machine Learning Research 11, 2169–2173 (2010)MATHGoogle Scholar
  22. 22.
    McAuley, J., de Campos, T., Csurka, G., Perronnin, F.: Hierarchical image-region labeling via structured learning. In: Proceedings of the British Machine Vision Conference, pp. 49.1–49.11. BMVA Press (2009)Google Scholar
  23. 23.
    Cooper, G.F.: The computational complexity of probabilistic inference using bayesian belief networks. Artificial Intelligence 42(2), 393–405 (1990)CrossRefMATHMathSciNetGoogle Scholar
  24. 24.
    Najman, L., Cousty, J., Perret, B.: Playing with kruskal: Algorithms for morphological trees in edge-weighted graphs. In: International Symposium on Mathematical Morphology, pp. 135–146 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Abdullah Al-Dujaili
    • 1
    • 2
  • François Merciol
    • 1
  • Sébastien Lefèvre
    • 1
  1. 1.IRISAUniversity of Bretagne-SudVannesFrance
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore

Personalised recommendations