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Issue No.12 - December (2009 vol.31)
pp: 2290-2297
Alex Levinshtein , University of Toronto, Toronto
Adrian Stere , University of Toronto, Toronto
Kiriakos N. Kutulakos , University of Toronto, Toronto
David J. Fleet , University of Toronto, Toronto
Sven J. Dickinson , University of Toronto, Toronto
Kaleem Siddiqi , McGill University, Montreal
ABSTRACT
We describe a geometric-flow-based algorithm for computing a dense oversegmentation of an image, often referred to as superpixels. It produces segments that, on one hand, respect local image boundaries, while, on the other hand, limiting undersegmentation through a compactness constraint. It is very fast, with complexity that is approximately linear in image size, and can be applied to megapixel sized images with high superpixel densities in a matter of minutes. We show qualitative demonstrations of high-quality results on several complex images. The Berkeley database is used to quantitatively compare its performance to a number of oversegmentation algorithms, showing that it yields less undersegmentation than algorithms that lack a compactness constraint while offering a significant speedup over N-cuts, which does enforce compactness.
INDEX TERMS
Superpixels, image segmentation, image labeling, perceptual grouping.
CITATION
Alex Levinshtein, Adrian Stere, Kiriakos N. Kutulakos, David J. Fleet, Sven J. Dickinson, Kaleem Siddiqi, "TurboPixels: Fast Superpixels Using Geometric Flows", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.31, no. 12, pp. 2290-2297, December 2009, doi:10.1109/TPAMI.2009.96
REFERENCES
[1] V. Caselles, F. Catte, T. Coll, and F. Dibos, “A Geometric Model for Active Contours in Image Processing,” Numerische Mathematik, vol. 66, pp. 1-31, 1993.
[2] V. Caselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours,” Proc. IEEE Int'l Conf. Computer Vision, pp. 694-699, 1995.
[3] D. Comaniciu and P. Meer, “Mean Shift: A Robust Approach toward Feature Space Analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.
[4] T. Cour, F. Benezit, and J. Shi, “Spectral Segmentation with Multiscale Graph Decomposition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 1124-1131, 2005.
[5] I. Cox, S. Rao, and Y. Zhong, “‘Ratio Regions’: A Technique for Image Segmentation,” Proc. Int'l Conf. Pattern Recognition, pp. 557-564, 1996.
[6] P. Felzenszwalb and D. Huttenlocher, “Efficient Graph-Based Image Segmentation,” Int'l J. Computer Vision, vol. 59, no. 2, pp. 167-181, 2004.
[7] L. Vincent and P. Soille, “Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583-598, June 1991.
[8] X. He, R. Zemel, and D. Ray, “Learning and Incorporating Top-Down Cues in Image Segmentation,” Proc. European Conf. Computer Vision, vol. 1, pp. 338-351, 2006.
[9] D. Hoiem, A. Efros, and M. Hebert, “Automatic Photo Pop-Up,” ACM Trans. Graphics, vol. 24, no. 3, pp. 577-584, 2005.
[10] D. Hoiem, A. Efros, and M. Hebert, “Geometric Context from a Single Image,” Proc. IEEE Int'l Conf. Computer Vision, pp. 654-661, 2005.
[11] I. 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.
[12] S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, “Gradient Flows and Geometric Active Contour Models,” Proc. IEEE Int'l Conf. Computer Vision, pp. 810-815, 1995.
[13] B. Kimia, A. Tannenbaum, and S. Zucker, “Toward a Computational Theory of Shape: An Overview,” Lecture Notes in Computer Science, vol. 427, pp. 402-407, Springer, 1990.
[14] R. Malladi, J. Sethian, and B. Vemuri, “Shape Modeling with Front Propagation: A Level Set Approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 158-175, Feb. 1995.
[15] D. Martin, C. Fowlkes, 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.
[16] G. Mori, X. Ren, A. Efros, and J. Malik, “Recovering Human Body Configurations: Combining Segmentation and Recognition,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 326-333, 2004.
[17] S. Osher and J. Sethian, “Fronts Propagation with Curvature Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations,” J. Computational Physics, vol. 79, pp. 12-49, 1988.
[18] X. Ren and J. Malik, “Learning a Classification Model for Segmentation,” Proc. IEEE Int'l Conf. Computer Vision, pp. 10-17, 2003.
[19] 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.
[20] E. Sharon, A. Brandt, and R. Basri, “Fast Multiscale Image Segmentation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 70-77, 2000.
[21] 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.
[22] K. Siddiqi, S. Bouix, A. Tannenbaum, and S. Zucker, “Hamilton-Jacobi Skeletons,” Int'l J. Computer Vision, vol. 48, no. 3, pp. 215-231, 2002.
[23] K. Siddiqi, Y. Lauzière, A. Tannenbaum, and S. Zucker, “Area and Length Minimizing Flows for Shape Segmentation,” IEEE Trans. Image Processing, vol. 7, no. 3, pp. 433-443, Mar. 1998.
[24] H. Tek and B. Kimia, “Image Segmentation by Reaction-Diffusion Bubbles,” Proc. IEEE Int'l Conf. Computer Vision, pp. 156-162, 1995.
[25] S. Wang and M. Siskind, “Image Segmentation with Ratio Cut—Supplemental Material,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 6, pp. 675-690, June 2003.
[26] Z. Wu and R. Leahy, “An Optimal Graph Theoretic Approach to Data Clustering: Theory and Application to Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1101-1113, Nov. 1993.
[27] S. Yu and J. Shi, “Multiclass Spectral Clustering,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 313-319, 2003.
[28] T.B. Sebastian, H. Tek, J.J. Crisco, S.W. Wolfe, and B.B. Kimia, “Segmentation of Carpal Bones from 3D CT Images Using Skeletally Coupled Deformable Models,” Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 1184-1194, 1998.
[29] L. Lorigo, O. Faugeras, W. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C. Westin, “Codimension-Two Geodesic Active Contours for the Segmentation of Tubular Structures,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 444-451, 2000.
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