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Image Retrieval Using Shape Contexts

Dr. A. M. Rajurkar, D. K. Kirange, Shubhangi D. Patil

Abstract


In this paper we have used shape models that are computationally fast and invariant to basic transformations like translation, rotation and scaling. This work drives shape detection using a feature called shape context. We demonstrate that shape context can be used to quickly prune similar shapes. Shape context describes all boundary points of a shape with respect to any single boundary point. The shape context at a reference point captures the distribution of the remaining points relative to centroid, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object.


Keywords


Shape Context, Centroid.

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References


Gerg Mori., S. Belongie, J. Malik, “Efficient Shape Matching Using Shape Context, ” IEEE Trans. Pattern Analysis and Machine Intelligence vol. 27, no. 11, November 2005.

I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding, ” Psychological Rev., vol. 94, no. 2, pp. 115-147, 1987.

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.

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. 09-415, 2003.

G. Borgefors, “Hierarchical Chamfer Matching: A Parametric Edge Matching lgorithm, ” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 6, pp. 849-865, 1988.

L. von Ahn, M. Blum, and J. Langford, “Telling Humans and Computers Apart Automatically), ” CMU Technical Report CMU-CS-02-117, Feb.2002.

M. Turk and A. Pentland, “Eigenfaces for Recognition, ” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-96, 1991.

M. Lades, C. Vorbru¨ggen, 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.

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.

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.

C. Burges and B. Scho¨lkopf, “Improving the Accuracy and Speed of Support Vector Machines, ” Advances in Neural Information Processing Systems, pp. 375-381, 1997.

B. Moghaddam, T. Jebara, and A. Pentland, “Bayesian Face Recognition, ” Pattern Recognition, vol. 33, no. 11, pp. 1771-1782, Nov. 2000.

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.

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.

D. Sharvit, J. Chan, H. Tek, and B. Kimia, “Symmetry-Based Indexing of Image Databases, ” J. Visual Comm. and Image Representation, June 1998.

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.

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.

D. Gavrila and V. Philomin, “Real-Time Object Detection for Smart Vehicles, ” Proc. Seventh Int’l Conf. Computer Vision, pp. 87-93, 1999.

S. Carlsson, “Order Structure, Correspondence and Shape Based Categories, ” Shape Contour and Grouping in Computer Vision, pp.58-71, 1999.

A. E. J ohnson and M. Hebert, “Recognizing Objects by Matching Oriented Points, ” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 684- 689, 1997.

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.

D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” Int’l J. Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

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.

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.

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.

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.

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.

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.

P. Indyk and R. Motwani, “Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality, ” Proc. ACM Symp. Theory of Computing, pp. 604-613, 1998.

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.

D. Martin, C. Fowlkes, and J. Malik, “Learning to Find Brightness and Texture Boundaries in Natural Images, ” Advances in Neural Information Processing Systems, 2002.

I. Biederman, “Recognition-by-Components: A Theory of Human Image Understanding, ” Psychological Rev., vol. 94, no. 2, pp. 115-147, 1987.

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.

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.

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.

Hao Zhang Jitendra Malik, “Learning a discriminative classifier using shape context distances”, University of California at Berkeley Berkeley,CA 94720-1776


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