|
Published Articles >> Table of Contents >> Abstract
October 2006 (Vol. 28, No. 10)
pp. 1701-1706
Iterative Local-Global Energy Minimization for Automatic Extraction of Objects of Interest
Gang Hua, IEEE
Zicheng Liu, IEEE
Zhengyou Zhang, IEEE
Ying Wu, IEEE
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.209
Send link to a friend
| Abstract |
|
We propose a novel global-local variational energy to automatically extract objects of interest from images. Previous formulations only incorporate local region potentials, which are sensitive to incorrectly classified pixels during iteration. We introduce a global likelihood potential to achieve better estimation of the foreground and background models and, thus, better extraction results. Extensive experiments demonstrate its efficacy.
|
 References
|
[1] S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Recognition and Machine Intelligence, vol. 18, no. 9, pp. 884-900, Sept. 1996.
[2] N. Paragios and R. Deriche, “Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation,” Int'l J. Computer Vision, pp. 223-247, 2002.
[3] A. Blake, C. Rother, M. Brown, P. Pérez, and P. Torr, “Interactive Image Segmentation Using an Adaptive Gaussian Mixture MRF Model,” Proc. Eighth European Conf. Computer Vision, pp. 428-441, 2004.
[4] C. Rother, V. Kolmogorov, and A. Blake, “`Grabcut'— Interactive Foreground Extraction Using Iterated Graph Cuts,” ACM Trans. Graphics (Proc. SIGGRAPH '04), pp. 309-314, 2004.
[5] M. Rousson, T. Brox, and R. Deriche, “Active Unsupervised Texture Segmentation on a Diffusion Based Feature Space,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 699-704, June 2003.
[6] M. Kass, A. Witkin, and D. Terzopoulos, “Snakes: Active Contour Models,” Int'l J. Computer Vision, vol. 1, pp. 321-331, 1987.
[7] D. Mumford and J. Shah, “Optimal Approximations by Piecewise Smooth Functions and Associated Variational Problem,” Comm. Pure and Applied Math., vol. 42, pp. 577-584, 1989.
[8] A. Tsai, J.A. Yezzi, and A.S. Willsky, “A Curve Evolution Approach to Smoothing and Segmentation Using the Mumford-Shah Functional,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 1119-1124, June 2000.
[9] L.D. Cohen and I. Cohen, “Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1131-1147, Nov. 1993.
[10] V. Casselles, R. Kimmel, and G. Sapiro, “Geodesic Active Contours,” Int'l J. Computer Vision, vol. 22, no. 1, pp. 61-79, 1997.
[11] N. Paragios, O. Mellina Gottardo, and V. Ramesh, “Gradient Vector Flow Fast Geodesic Active Contours,” Proc. IEEE Int'l Conf. Computer Vision, pp. 67-73, July 2001.
[12] J.A. Yezzi, A. Tsai, and A.S. Willsky, “A Statistical Approach to Snakes for Bimodal and Trimodal Imagery,” Proc. IEEE Int'l Conf. Computer Vision, pp. 898-903, Sept. 1999.
[13] T.F. Chan and L.A. Vese, “Active Contours without Edges,” IEEE Trans. Image Processing, vol. 10, no. 2, pp. 266-277, Feb. 2001.
[14] S. Jehan-Besson, M. Barlaud, and G. Aubert, “Video Object Segmentation Using Eulerian Region-Based Active Contours,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 353-361, July 2001.
[15] J. Kim, J.W. FisherIII, A.J. Yezzi, M. Çetin, and A.S. Willsky, “A Nonparametric Statistical Method for Image Segmentation Using Information Theory and Curve Evolution,” IEEE Trans. Image Processing, vol. 14, no. 10, pp. 1486-1502, Oct. 2005.
[16] N. Paragios and R. Deriche, “Geodesic Active Contours for Supervised Texture Segmentation,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 1034-1040, 1999.
[17] S. Jehan-Besson, M. Barlaud, and G. Aubert, “Shape Gradients for Histogram Segmentation Using Active Contours,” Proc. IEEE Int'l Conf. Computer Vision, vol. 1, pp. 408-415, Oct. 2003.
[18] C. Samson, L. Blanc-Féraud, G. Aubert, and J. Zerubia, “A Level Set Model for Image Classification,” Int'l J. Computer Vision, vol. 40, no. 3, pp. 187-197, Mar. 2000.
[19] S. Osher and J.A. Sethian, “Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulation,” J. Computational Physics, vol. 79, pp. 12-49, 1988.
[20] R. Malladi, J.A. Sethian, and B.C. 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.
[21] N.D. Lawrence and B. Schölkopf, “Estimating a Kernel Fisher Discriminant in the Presence of Label Noise,” Proc. Int'l Conf. Machine Learning, C. Brodley and A.P. Danyluk, eds., pp. 306-313, 2001.
[22] A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statistical Soc., Series B, vol. 39, no. 1, pp. 1-38, 1977.
[23] A. Corduneanu and T. Jaakkola, “Continuation Methods for Mixing Heterogenous Sources,” Proc. Uncertainty in Artificial Intelligence Conf., pp. 111-118, 2002.
[24] X. Zhu, J. Yang, and A. Waibel, “Segmenting Hands of Arbitrary Color,” Proc. IEEE Int'l Conf. Automatic Face Recognition, pp. 446-453, Mar. 2000.
[25] 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. 1-18, May 2002.
[26] D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics,” Proc. Eighth IEEE Int'l Conf. Computer Vision, vol. 2, pp. 416-423, July 2001.
|
 Additional Information
|
Index Terms- Variational energy, level set, semisupervised learning.
Citation:
Gang Hua, Zicheng Liu, Zhengyou Zhang, Ying Wu,
"Iterative Local-Global Energy Minimization for Automatic Extraction of Objects of Interest,"
IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 28,
no. 10,
pp. 1701-1706,
Oct.,
2006
|
|
RSS Feed