This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Graph Partitioning Active Contours (GPAC) for Image Segmentation
April 2006 (vol. 28 no. 4)
pp. 509-521
Baris Sumengen, IEEE Computer Society
In this paper, we introduce new types of variational segmentation cost functions and associated active contour methods that are based on pairwise similarities or dissimilarities of the pixels. As a solution to a minimization problem, we introduce a new curve evolution framework, the graph partitioning active contours (GPAC). Using global features, our curve evolution is able to produce results close to the ideal minimization of such cost functions. New and efficient implementation techniques are also introduced in this paper. Our experiments show that GPAC solution is effective on natural images and computationally efficient. Experiments on gray-scale, color, and texture images show promising segmentation results.

[1] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours,” Int'l J. Computer Vision, pp. 61-79, Feb. 1997.
[2] N. Xu, R. Bansal, and N. Ahuja, “Object Segmentation Using Graph Cuts Based Active Contours,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 46-53, June 2003.
[3] Y. Boykov and V. Kolmogorov, “Computing Geodesics and Minimal Surfaces via Graph Cuts,” Proc. IEEE Int'l Conf. Computer Vision (ICCV), pp. 26-33, Oct. 2003.
[4] D. Mumford and J. Shah, “Boundary Detection by Minimizing Functionals,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 22-26, 1985.
[5] T.F. Chan and L.A. Vese, “Active Contours without Edges,” IEEE Trans. Image Processing, pp. 266-277, Feb. 2001.
[6] L.D. Cohen, “On Active Contour Models and Balloons,” Computer Vision, Graphics, and Image Processing. Image Understanding, vol. 53, no. 2, pp. 211-218, 1991.
[7] A. Tsai, “Curve Evolution and Estimation-Theoretic Techniques for Image Processing,” PhD thesis, Harvard-MIT Division of Health Sciences and Tech nology, Aug. 2000.
[8] A. Tsai, A.J. Yezzi, and A.S. Willsky, “Curve Evolution Implementation of the Mumford-Shah Functional for Image Segmentation, Denoising, Interpolation, and Magnification,” IEEE Trans. Image Processing, pp. 1169-1186, Aug. 2001.
[9] A.J. Yezzi, A. Tsai, and A. Willsky, “A Statistical Approach to Snakes for Bimodal and Trimodal Imagery,” Proc. Int'l Conf. Computer Vision (ICCV), pp. 898-903, 1999.
[10] N. Paragios and R. Deriche, “Geodesic Active Regions: A New Framework to Deal with Frame Partition Problems in Computer Vision,” J. Visual Comm. and Image Representation, pp. 249-268, Mar. 2002.
[11] 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.
[12] Z. Wu and R. Leahy, “An Optimal Graph Theoretic Approach to Data Clustering: Theory and Its Application to Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1101-1113, Nov. 1993.
[13] S. Sarkar and P. Soundararajan, “Supervised Learning of Large Perceptual Organization: Graph Spectral Partitioning and Learning Automata,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 5, pp. 504-525, May 2000.
[14] S. Wang and J.M. Siskind, “Image Segmentation with Ratio Cut,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 675-690, June 2003.
[15] 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.
[16] I.H. Jermyn and H. Ishikawa, “Globally Optimal Regions and Boundaries as Minimum Ratio Weight Cycles,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 10, pp. 1075-1088, Oct. 2001.
[17] S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
[18] A. Vasilevskiy and K. Siddiqi, “Flux Maximizing Geometric Flows,” Proc. IEEE Int'l Conf. Computer Vision, pp. 7-14, July 2001.
[19] B. Sumengen and B.S. Manjunath, “Category Pruning in Image Databases Using Segmentation and Distance Maps,” Proc. European Signal Processing Conf. (EUSIPCO), Sept. 2005.
[20] J.A. Sethian, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge Univ. Press, 1999.
[21] I.J. Cox, S.B. Rao, and Y. Zhong, “Ratio Regions: A Technique for Image Segmentation,” Proc. Int'l Conf. Pattern Recognition (ICPR), pp. 557-564, Aug. 1996.
[22] P. Soundararajan and S. Sarkar, “An In-Depth Study of Graph Partitioning Measures for Perceptual Organization,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 642-660, June 2003.
[23] http://www.hid.ri.cmu.edu/Hidsoftware_ncutPublic.html , 2005.
[24] E. Sharon, A. Brandt, and R. Basri, “Fast Multiscale Image Segmentation,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 70-77, June 2000.
[25] E. Sharon, A. Brandt, and R. Basri, “Segmentation and Boundary Detection Using Multiscale Intensity Measurements,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 469-476, Dec. 2001.
[26] C. Fowlkes, S. Belongie, F. Chung, and J. Malik, “Spectral Grouping Using the Nystrom Method,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 214-225, Feb. 2004.
[27] G. Sapiro, Geometric Partial Differential Equations and Image Analysis. Cambridge Univ. Press, Jan. 2001.
[28] B.S. Manjunath and W.Y. Ma, “Texture Features for Browsing and Retrieval of Image Data,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 837-42, Aug. 1996.
[29] B. Sumengen and B.S. Manjunath, “Edgeflow-Driven Variational Image Segmentation: Theory and Performance Evaluation,” technical report, Aug. 2005.
[30] J. Malik, S. Belongie, J. Shi, and T. Leung, “Textons, Contours, and Regions: Cue Integration in Image Segmentation,” Proc. IEEE Int'l Conf. Computer Vision (ICCV), pp. 918-925, Sept. 1999.

Index Terms:
Curve evolution, active contours, image segmentation, pairwise similarity measures, graph partitioning.
Citation:
Baris Sumengen, B.S. Manjunath, "Graph Partitioning Active Contours (GPAC) for Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 509-521, April 2006, doi:10.1109/TPAMI.2006.76
Usage of this product signifies your acceptance of the Terms of Use.