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Rigid Shape Matching by Segmentation Averaging
April 2010 (vol. 32 no. 4)
pp. 619-635
ASCII Text
x 
 
Hongzhi Wang, John Oliensis, "Rigid Shape Matching by Segmentation Averaging," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 619-635, April, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.199,
author = {Hongzhi Wang and John Oliensis},
title = {Rigid Shape Matching by Segmentation Averaging},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {4},
issn = {0162-8828},
year = {2010},
pages = {619-635},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.199},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Rigid Shape Matching by Segmentation Averaging
IS - 4
SN - 0162-8828
SP619
EP635
EPD - 619-635
A1 - Hongzhi Wang,
A1 - John Oliensis,
PY - 2010
KW - Shape matching
KW - image segmentation
KW - mutual information.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Hongzhi Wang, University of Pennsylvania, Philadelphia
John Oliensis, Stevens Institute of Technology, Hoboken
We use segmentations to match images by shape. The new matching technique does not require point-to-point edge correspondence and is robust to small shape variations and spatial shifts. To address the unreliability of segmentations computed bottom-up, we give a closed form approximation to an average over all segmentations. Our method has many extensions, yielding new algorithms for tracking, object detection, segmentation, and edge-preserving smoothing. For segmentation, instead of a maximum a posteriori approach, we compute the “central” segmentation minimizing the average distance to all segmentations of an image. For smoothing, instead of smoothing images based on local structures, we smooth based on the global optimal image structures. Our methods for segmentation, smoothing, and object detection perform competitively, and we also show promising results in shape-based tracking.

[1] S. Agarwal and D. Roth, "Learning a Sparse Representation for Object Detection," Proc. European Conf. Computer Vision, 2002.
[2] N. Ahuja and S. Todorovic, "Learning the Taxonomy and Models of Categories Present in Arbitrary Images," Proc. Int'l Conf. Computer Vision, 2007.
[3] H. Barrow, J. Tenenbaum, R. Bolles, and H. Wolf, "Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching," Proc. Fifth Int'l Joint Conf. Artificial Intelligence, 1977.
[4] R. Basri and D. Jacobs, "Recognition Using Region Correspondences," Int'l J. Computer Vision, vol. 25, pp. 145-166, 1997.
[5] 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.
[6] A. Berg and J. Malik, "Geometric Blur for Template Matching," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[7] A. Berg, T. Berg, and J. Malik, "Shape Matching and Object Recognition Using Low Distortion Correspondences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[8] A. Bosch, A. Zisserman, and X. Munoz, "Representing Shape with a Spatial Pyramid Kernel," Proc. ACM Int'l Conf. Image and Video Retrieval, 2007.
[9] E. Borenstein and S. Ullman, "Combined Top-Down/Bottom-Up Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 12, pp. 2109-2125, Dec. 2008.
[10] Y. Boykov, O. Veksler, and R. Zabih, "Fast Approximate Energy Minimization via Graph Cuts," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222-1239, Nov. 2001.
[11] G. Charpiat, O. Faugeras, and R. Keriven, "Shape Statistics for Image Segmentation with Prior," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2007.
[12] D. Comaniciu and P. Meer, "Mean Shift: A Robust Approach towards Feature Space Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[13] N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[14] H.Q. Dinh and S. Kropac, "Multi-Resolution Spin Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[15] P. Felzenszwalb and D. Huttenlocher, "Efficient Graph-Based Image Segmentation," Int'l J. Computer Vision, vol. 59, no. 2, pp. 109-131, 2004.
[16] V. Ferrari, T. Tuytelaars, and L. Van Cool, "Simultaneous Object Recognition and Segmentation from Single or Multiple Model Views," Int'l J. Computer Vision, vol. 67, no. 2, pp. 159-188, 2006.
[17] V. Ferrari, L. Fevrier, F. Jurie, and C. Schmid, "Groups of Adjacent Contour Segments for Object Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 1, pp. 36-51, Jan. 2008.
[18] Y. Gdalyahu, D. Weinshall, and M. Werman, "Self Organization in Vision: Stochastic Clustering for Image Segmentation, Perceptual Grouping, and Image Database," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1053-1074, Oct. 2001.
[19] C.-Y. Huang, O.I. Camps, and T. Kanungo, "Object Recognition Using Appearance-Based Parts and Relations," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1997.
[20] D. Huttenlocher, G. Klanderman, and W. Rucklidge, "Comparing Images Using the Hausdorff Distance," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sept. 1993.
[21] A.E. Johnson and M. Hebert, "Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 433-449, May 1999.
[22] N. Jojic and Y. Caspi, "Capturing Image Structure with Probabilistic Index Maps," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[23] S. Joshi, B. Davis, M. Jomier, and G. Gerig, "Unbiased Diffeomorphic Atlas Construction for Computational Anatomy," NeuroImage, vol. 23, pp. 15-160, 2004.
[24] F. Jurie and B. Triggs, "Creating Efficient Codebooks for Visual Recognition," Proc. Int'l Conf. Computer Vision, 2005.
[25] S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[26] B. Leibe, A. Leonardis, and B. Schiele, "Combined Object Categorization and Segmentation with an Implicit Shape Model," Proc. European Conf. Computer Vision Workshop Statistical Learning in Computer Vision, 2004.
[27] A. Levin and Y. Weiss, "Learning to Combine Bottom-Up and Top-Down Segmentation," Int'l J. Computer Vision, vol. 81, no. 1, pp. 105-118, 2009.
[28] D. Lowe, "Distinctive Image Features from Scale Invariant Keypoints," Int'l J. Computer Vision, vol. 60, pp. 91-110, 2004.
[29] D. Martin, C. Fowlkes, D. Tal, and J. Malik, "A Database of Human Segmented Natural Images and Its Applications to Evaluating Segmentation Algorithms and Measuring Ecological Statistics," Proc. Int'l Conf. Computer Vision, 2001.
[30] D. Martin, C. Fowlkes, D. Tal, and J. Malik, "Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 530-549, May 2004.
[31] M. Meila, "Comparing Clusterings by the Variation of Information," Proc. Conf. Learning Theory, 2003.
[32] A. Opelt, A. Pinz, and A. Zisserman, "A Boundary-Fragment-Model for Object Detection," Proc. European Conf. Computer Vision, 2006.
[33] A. Opelt, A. Pinz, and A. Zisserman, "Fusing Shape and Appearance Information for Object Category Detection," Proc. British Machine Vision Conf., 2006.
[34] P. Perona and J. Malik, "Scale-Space and Edge Detection Using Anisotropic Diffusion," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, July 1990.
[35] X. Ren and J. Malik, "Learning a Classification Model for Segmentation," Proc. Int'l Conf. Computer Vision, 2003.
[36] D. Russakoff, C. Tomasi, T. Rohlfing, and C. Maurer, "Image Similarity Using Mutual Information of Regions," Proc. European Conf. Computer Vision, 2004.
[37] D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang, "Incremental Learning for Robust Visual Tracking," Int'l J. Computer Vision, vol. 77, pp. 125-141, 2007.
[38] B. Russell, A. Efros, J. Sivic, W. Freeman, and A. Zisserman, "Using Multiple Segmentations to Discover Objects and Their Extent in Image Collections," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[39] E. Sharon, M. Galun, D. Sharon, R. Basri, and A. Brandt, "Hierarchy and Adaptivity in Segmenting Visual Scenes," Nature, vol. 442, pp. 810-813, 2006.
[40] J. Shi and J. Malik, "Normalized Cuts and Image Segmentation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[41] J. Shotton, A. Blake, and R. Cipolla, "Multi-Scale Categorical Object Recognition Using Contour Fragments," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1270-1281, July 2008.
[42] K. Siddiqi, A. Shokoufandeh, S. Dickinson, and S. Zucker, "Shock Graphs and Shape Matching," Int'l J. Computer Vision, vol. 35, pp. 13-32, 1999.
[43] C. Stauffer and E. Grimson, "Similarity Templates for Detection and Recognition," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[44] H. Sundar, D. Silver, N. Gagvani, and S. Dickinson, "Skeleton Based Shape Matching and Retrieval," Proc. Shape Modeling Int'l, 2003.
[45] C. Tomasi and R. Manduchi, "Bilateral Filtering for Gray and Color Images," Proc. Int'l Conf. Computer Vision, 1998.
[46] R. Unnikrishnan, C. Pantofaru, and M. Hebert, "Toward Objective Evaluation of Image Segmentation Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 929-944, June 2007.
[47] P. Viola and W.M. Wells III, "Alignment by Maximization of Mutual Information," Int'l J. Computer Vision, vol. 24, no. 2, pp. 137-154, 1997.
[48] H. Wang and J. Oliensis, "Shape Matching by Segmentation Averaging," Proc. European Conf. Computer Vision, 2008.
[49] J. Wang, J. Li, and G. Wiederhold, "SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture Libraries," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 9, pp. 947-963, Sept. 2001.
[50] A. Yang, J. Wright, Y. Ma, and S. Sastry, "Unsupervised Segmentation of Natural Images via Lossy Data Compression," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 212-225, 2008.
[51] W.E. Young and R.H. Trent, "Geometric Mean Approximation of Individual Security and Portfolio Performance," J. Financial and Quantitative Analysis, vol. 4, pp. 179-199, 1969.
[52] S.X. Yu, R. Gross, and J. Shi, "Concurrent Object Recognition and Segmentation by Graph Partitioning," Proc. Neural Information Processing Systems, 2002.
[53] L. Zhu, C. Lin, H. Huang, Y. Chen, and A. Yuille, "Unsupervised Structure Learning: Hierarchical Recursive Composition, Suspicious Coincidence and Competitive Exclusion," Proc. European Conf. Computer Vision, 2008.
[54] S.C. Zhu and A. Yuille, "FORMS: A Flexible Object Recognition and Modelling System," Int'l J. Computer Vision, vol. 20, pp. 187-212, 1996.

Index Terms:
Shape matching, image segmentation, mutual information.
Citation:
Hongzhi Wang, John Oliensis, "Rigid Shape Matching by Segmentation Averaging," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 619-635, April 2010, doi:10.1109/TPAMI.2009.199
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