Learning Hierarchical Shape Models from Examples

  • Alex Levinshtein
  • Cristian Sminchisescu
  • Sven Dickinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


We present an algorithm for automatically constructing a decompositional shape model from examples. Unlike current approaches to structural model acquisition, in which one-to-one correspondences among appearance-based features are used to construct an exemplar-based model, we search for many-to-many correspondences among qualitative shape features (multi-scale ridges and blobs) to construct a generic shape model. Since such features are highly ambiguous, their structural context must be exploited in computing correspondences, which are often many-to-many. The result is a Marr-like abstraction hierarchy, in which a shape feature at a coarser scale can be decomposed into a collection of attached shape features at a finer scale. We systematically evaluate all components of our algorithm, and demonstrate it on the task of recovering a decompositional model of a human torso from example images containing different subjects with dissimilar local appearance.


Decompositional Model Abstraction Hierarchy Spherical Code Node Correspondence Attachment Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Fei-Fei, L., Fergus, R., Perona, P.: A Bayesian Approach to Unsupervised One-Shot Learning of Object Categories. In: ICCV, Nice (2003)Google Scholar
  2. 2.
    Lazebnik, S., Schmid, C., Ponce, J.: Semi-Local Affine Parts for Object Recognition. In: BMVC (2004)Google Scholar
  3. 3.
    Song, Y., Goncalves, L., Perona, P.: Unsupervised Learning of Human Motion. IEEE PAMI 25(7) (2003)Google Scholar
  4. 4.
    Ramanan, D., Forsyth, D.A.: Using Temporal Coherence to Build Models of Animals. In: ICCV (2003)Google Scholar
  5. 5.
    Ramanan, D., Forsyth, D.A.: Finding and Tracking People From the Bottom Up. In: IEEE CVPR (2003)Google Scholar
  6. 6.
    Ioffe, S., Forsyth, D.A.: Human tracking with mixtures of trees. In: ICCV (2001)Google Scholar
  7. 7.
    Fergus, R., Perona, P., Zisserman, A.: Object Class Recognition by Unsupervised Scale-Invariant Learning. In: IEEE CVPR (2003)Google Scholar
  8. 8.
    Felzenszwalb, P., Huttenlocher, D.: Efficient Matching of Pictorial Structures. In: IEEE CVPR (2000)Google Scholar
  9. 9.
    Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Info. Theory IT-14(3), 462–467 (1968)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Brooks, R.A.: Model-Based Three Dimensional Interpretations of Two Dimensional Images. IEEE PAMI (March 1983)Google Scholar
  11. 11.
    Marr, D., Nishihara, H.K.: Representation and recognition of the spatial organization of three dimensional shapes. Proc. of Royal Soc. of London (1978)Google Scholar
  12. 12.
    Jepson, A.D., Fleet, D.J., Black, M.J.: A layered motion representation with occlusion and compact spatial support. In: ECCV (2002)Google Scholar
  13. 13.
    Viola, P., Jones, M.J., Snow, D.: Detecting Pedestrians Using Patterns of Motion and Appearance. In: ICCV (2003)Google Scholar
  14. 14.
    Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: ICCV (1998)Google Scholar
  15. 15.
    Cohen, S., Guibas, L.J.: The Earth Mover’s Distance under Transformation Sets. In: ICCV (1999)Google Scholar
  16. 16.
    Demirci, M.F., Shokoufandeh, A., Dickinson, S., Keselman, Y., Bretzner, L.: Many-to-Many Feature Matching Using Spherical Coding of Directed Graphs. In: ECCV (2004)Google Scholar
  17. 17.
    Matoušek, J.: On Embedding Trees into Uniformly Convex Banach Spaces. Israel J. of Mathematics 237 (1999)Google Scholar
  18. 18.
    Lindeberg, T., Bretzner, L.: Real-time scale selection in hybrid multi-scale representations. In: Proc. Scale-Space 2003 (2003)Google Scholar
  19. 19.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(5500) (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alex Levinshtein
    • 1
  • Cristian Sminchisescu
    • 1
    • 2
  • Sven Dickinson
    • 1
  1. 1.University of TorontoCanada
  2. 2.TTI-CChicagoUSA

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