This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Efficient Shape Matching Using Shape Contexts
November 2005 (vol. 27 no. 11)
pp. 1832-1837
We demonstrate that shape contexts can be used to quickly prune a search for similar shapes. We present two algorithms for rapid shape retrieval: representative shape contexts, performing comparisons based on a small number of shape contexts, and shapemes, using vector quantization in the space of shape contexts to obtain prototypical shape pieces.

[1] S. Thorpe, D. Fize, and C. Marlot, “Speed of Processing in the Human Visual System,” Nature, vol. 381, pp. 520-522, 1996.
[2] I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding,” Psychological Rev., vol. 94, no. 2, pp. 115-147, 1987.
[3] S. Belongie, J. Malik, and J. Puzicha, “Shape Matching and Object Recognition Using Shape Contexts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 4, pp. 509-522, Apr. 2002.
[4] B. Leibe and B. Schiele, “Analyzing Appearance and Contour Based Methods for Object Categorization,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 409-415, 2003.
[5] J.G. Snodgrass and M. Vanderwart, “A Standardized Set of 260 Pictures: Norms for Name Agreement, Familiarity and Visual Complexity,” J. Experimental Psychology: Human Learning and Memory, vol. 6, pp. 174-215, 1980.
[6] L. von Ahn, M. Blum, and J. Langford, “Telling Humans and Computers Apart (Automatically),” CMU Technical Report CMU-CS-02-117, Feb. 2002.
[7] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-96, 1991.
[8] M. Lades, C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. Wurtz, and W. Konen, “Distortion Invariant Object Recognition in the Dynamic Link Architecture,” IEEE Trans. Computers, vol. 42, no. 3, pp. 300-311, Mar. 1993.
[9] T. Cootes, D. Cooper, C. Taylor, and J. Graham, “Active Shape Models— Their Training and Application,” Computer Vision and Image Understanding, vol. 61, no. 1, pp. 38-59, Jan. 1995.
[10] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
[11] C. Burges and B. Schölkopf, “Improving the Accuracy and Speed of Support Vector Machines,” Advances in Neural Information Processing Systems, pp. 375-381, 1997.
[12] B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian Face Recognition,” Pattern Recognition, vol. 33, no. 11, pp. 1771-1782, Nov. 2000.
[13] H. Murase and S. Nayar, “Visual Learning and Recognition of 3-D Objects from Appearance,” Int'l J. Computer Vision, vol. 14, no. 1, pp. 5-24, Jan. 1995.
[14] C. Zahn and R. Roskies, “Fourier Descriptors for Plane Closed Curves,” IEEE Trans. Computers, vol. 21, no. 3, pp. 269-281, Mar. 1972.
[15] D. Sharvit, J. Chan, H. Tek, and B. Kimia, “Symmetry-Based Indexing of Image Databases,” J. Visual Comm. and Image Representation, June 1998.
[16] G. Borgefors, “Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 849-865, 1988.
[17] D. Huttenlocher, R. Lilien, and C. Olson, “View-Based Recognition Using an Eigenspace Approximation to the Hausdorff Measure,” Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 9, pp. 951-955, Sept. 1999.
[18] D. Gavrila and V. Philomin, “Real-Time Object Detection for Smart Vehicles,” Proc. Seventh Int'l Conf. Computer Vision, pp. 87-93, 1999.
[19] S. Carlsson, “Order Structure, Correspondence and Shape Based Categories,” Shape Contour and Grouping in Computer Vision, pp. 58-71, 1999.
[20] A.E. Johnson and M. Hebert, “Recognizing Objects by Matching Oriented Points,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 684-689, 1997.
[21] H. Chui and A. Rangarajan, “A New Algorithm for Non-Rigid Point Matching,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 44-51, June 2000.
[22] D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int'l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
[23] R. Fergus, P. Perona, and A. Zisserman, “Object Class Recognition by Unsupervised Scale-Invariant Learning,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 264-271, 2003.
[24] G. Dorko and C. Schmid, “Selection of Scale Invariant Neighborhoods for Object Class Recognition,” Proc. Ninth Int'l Conf. Computer Vision, pp. 634-640, 2003.
[25] Y. Amit, D. Geman, and K. Wilder, “Joint Induction of Shape Features and Tree Classifiers,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 11, pp. 1300-1305, Nov. 1997.
[26] J. Beis and D. Lowe, “Shape Indexing Using Approximate Nearest-Neighbour Search in Highdimensional Spaces,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1000-1006, 1997.
[27] T. Sebastian, P.N. Klein, and B.B. Kimia, “Shock-Based Indexing into Large Shape Databases,” Proc. European Conf. Computer Vision, vol. 3, pp. 731-746, 2002.
[28] G. Shakhnarovich, P. Viola, and T. Darrell, “Fast Pose Estimation with Parameter Sensitive Hashing,” Proc. Ninth Int'l Conf. Computer Vision, vol. 2, pp. 750-757, 2003.
[29] P. Indyk and R. Motwani, “Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality,” Proc. ACM Symp. Theory of Computing, pp. 604-613, 1998.
[30] A. Frome, D. Huber, R. Kolluri, T. Bulow, and J. Malik, “Recognizing Objects in Range Data Using Regional Point Descriptors,” Proc. Eighth European Conf. Computer Vision, vol. 3, pp. 224-237, 2004.
[31] D. Martin, C. Fowlkes, and J. Malik, “Learning to Find Brightness and Texture Boundaries in Natural Images,” Advances in Neural Information Processing Systems, 2002.
[32] F.L. Bookstein, “Principal Warps: Thin-Plate Splines and Decomposition of Deformations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 567-585, June 1989.
[33] G. Mori and J. Malik, “Recognizing Objects in Adversarial Clutter: Breaking a Visual CAPTCHA,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 134-141, 2003.
[34] A. Thayananthan, B. Stenger, P.H.S. Torr, and R. Cipolla, “Shape Context and Chamfer Matching in Cluttered Scenes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 127-133, June 2003.
[35] G. Mori, S. Belongie, and J. Malik, “Shape Contexts Enable Efficient Retrieval of Similar Shapes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 723-730, 2001.

Index Terms:
Index Terms- Shape, object recognition, optical character recognition.
Citation:
Greg Mori, Serge Belongie, Jitendra Malik, "Efficient Shape Matching Using Shape Contexts," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 11, pp. 1832-1837, Nov. 2005, doi:10.1109/TPAMI.2005.220
Usage of this product signifies your acceptance of the Terms of Use.