Abstract
We present an algorithm for shape matching and recognition based on a generative model for how one shape can be generated by the other. This generative model allows for a class of transformations, such as affine and non-rigid transformations, and induces a similarity measure between shapes. The matching process is formulated in the EM algorithm. To have a fast algorithm and avoid local minima, we show how the EM algorithm can be approximated by using informative features, which have two key properties–invariant and representative. They are also similar to the proposal probabilities used in DDMCMC [13]. The formulation allows us to know when and why approximations can be made and justifies the use of bottom-up features, which are used in a wide range of vision problems. This integrates generative models and feature-based approaches within the EM framework and helps clarifying the relationships between different algorithms for this problem such as shape contexts [3] and softassign [5]. We test the algorithm on a variety of data sets including MPEG7 CE-Shape-1, Kimia silhouettes, and real images of street scenes. We demonstrate very effective performance and compare our results with existing algorithms. Finally, we briefly illustrate how our approach can be generalized to a wider range of problems including object detection.
Chapter PDF
References
Abbasi, S., Mokh, F.: Robustness of Shape Similarity Retrieval under Affine. In: Proc. of Challenge of Image Retrieval (1999)
Ballard, D.H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 13(2) (1981)
Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Trans. on PAMI 24(24) (2002)
Bookstein, F.L.: Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE Trans. on PAMI 11(6) (1989)
Chui, H., Rangarajan, A.: A New Point Matching Algorithm for Non-rigid Registration. Computer Vision and Image Understanding (March 2003)
Grenander, U.: General Pattern Theory: A Mathematical Study of Regular Structures, Oxford (1994)
Latechi, L.J., Lakamper, R., Eckhardt, U.: Shape Descriptors for Non-rigid Shapes with a Single Closed Contour. In: Proc. of CVPR (2000)
Neal, R., Hinton, G.E.: A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants. Learning in Graphical Models (1998)
Rangarajan, A., Coughlan, J.M., Yuille, A.L.: A Bayesian Network for Relational Shape Matching. In: Proc. of ICCV, Nice, France (2003)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: On Aligning Curves. IEEE Trans. on PAMI 25(1) (2003)
Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing their Shock Graphs, accepted by IEEE Trans. on PAMI (2003)
Thayananthan, A., Stenger, B., Torr, P.H.S., Cipolla, R.: Shape Context and Chamfer Matching in Cluttered Scenes. In: CVPR (2003)
Tu, Z., Chen, X., Yuille, A., Zhu, S.C.: Image Parsing: Unifying Segmentation, Detection and Recognition. In: Proc. of ICCV, Nice, France (2003)
Veltkamp, R.C., Hagedoorn, M.: State of the Art in Shape Matching, Technical Report UU-CS-1999-27, Utrecht (1999)
Yuille, A.L., Grzywacz, N.M.: A Computational Theory for the Perception of Coherent Visual Motion. Nature 333(6168) (1988)
Zhu, S.C., Yuille, A.L.: FORMS: A Flexible Object Recognition and Modeling System. IJCV 20(3) (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tu, Z., Yuille, A.L. (2004). Shape Matching and Recognition – Using Generative Models and Informative Features. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24672-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-540-24672-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21982-8
Online ISBN: 978-3-540-24672-5
eBook Packages: Springer Book Archive