Flexible Multi-modal Graph-Based Segmentation

  • Willem P. Sanberg
  • Luat Do
  • Peter H. N. de With
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


This paper aims at improving the well-known local variance segmentation method by adding extra signal modi and specific processing steps. As a key contribution, we extend the uni-modal segmentation method to perform multi-modal analysis, such that any number of signal modi available can be incorporated in a very flexible way. We have found that the use of a combined weight of luminance and depth values improves the segmentation score by 6.8%, for a large and challenging multi-modal dataset. Furthermore, we have developed an improved uni-modal texture-segmentation algorithm. This improvement relies on a clever choice of the color space and additional pre- and post-processing steps, by which we have increased the segmentation score on a challenging texture dataset by 2.1%. This gain is mainly preserved when using a different dataset with worse lighting conditions and different scene types.


Multi-modal Signal Analysis RGBD Segmentation Graphs 


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  1. 1.
    Alcantarilla, P.F., Bartoli, A., Davison, A.J.: KAZE Features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. ECCV, vol. 7577, pp. 214–227. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  2. 2.
    Arbeláez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 33(5), 898–916 (2011)CrossRefGoogle Scholar
  3. 3.
    Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Comp. Soc. Conf. on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1124–1131. IEEE (2005)Google Scholar
  4. 4.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient Graph-Based Image Segmentation. Int. Journal of Computer Vision 59(2), 167–181 (2004)CrossRefGoogle Scholar
  5. 5.
    Kropatsch, W., Haxhimusa, Y., Ion, A.: Multiresolution image segmentations in graph pyramids. Applied Graph Theory in Computer Vision and Pattern Recognition 41(2), 3–41 (2007)CrossRefGoogle Scholar
  6. 6.
    Peng, B., Zhang, L., Zhang, D.: A survey of graph theoretical approaches to image segmentation. Pattern Recognition 46(3), 1020–1038 (2013)CrossRefGoogle Scholar
  7. 7.
    Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI) 12(7), 629–639 (1990)CrossRefGoogle Scholar
  8. 8.
    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Strom, J., Richardson, A., Olson, E.: Graph-based segmentation for colored 3D laser point clouds. In: IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2131–2136. IEEE, Taipei (2010)Google Scholar
  10. 10.
    Weickert, J.: Efficient image segmentation using partial differential equations and morphology. Pattern Recognition 34(9), 1813–1824 (1998)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Willem P. Sanberg
    • 1
  • Luat Do
    • 1
  • Peter H. N. de With
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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