Abstract
We introduce a novel approach to variational image segmentation with shape priors. Key properties are convexity of the joint energy functional and weak coupling of convex models from different domains by mapping corresponding solutions to a common space. Specifically, we combine total variation based continuous cuts for image segmentation and convex relaxations of Markov Random Field based shape priors learned from shape databases. A convergent algorithm amenable to large-scale convex programming is presented. Numerical experiments demonstrate promising synergistic performance of convex continuous cuts and convex variational shape priors under image distortions related to noise, occlusions and clutter.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Attouch, H., Bolte, J., Redont, P., Soubeyran, A.: Alternating proximal algorithms for weakly coupled convex minimization problems. Applications to dynamical games and PDE’s. Journal of Convex Analysis 15(3), 485–506 (2008)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Patt. Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision 40, 120–145 (2011)
Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J. Appl. Math. 66(5), 1632–1648 (2006)
Charpiat, G., Faugeras, O., Keriven, R.: Approximations of shape metrics and application to shape warping and empirical shape statistics. Found. Comp. Math. 5(1), 1–58 (2005)
Cremers, D., Kohlberger, T., Schnörr, C.: Shape statistics in kernel space for variational image segmentation. Patt. Recognition 36(9), 1929–1943 (2003)
Dambreville, S., Rathi, Y., Tannenbaum, A.: A framework for image segmentation using shape models and kernel space shape priors. IEEE Trans. Patt. Anal. Mach. Intell. 30(8), 1385–1399 (2008)
Deza, M.M., Laurent, M.: Geometry of Cuts and Metrics. Springer, Heidelberg (1997)
Esser, E., Zhang, X., Chan, T.: A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science. SIAM J. Imag. Sci. 3(4), 1015–1046 (2010)
Hoefling, H., Tibshirani, R.: Estimation of sparse binary pairwise markow networks using pseudo-likelihoods. Journal of Machine Learning Research 10, 883–906 (2009)
Lecumberry, F., Pardo, A., Sapiro, G.: Simultaneous object classification and segmentation with high-order multiple shape models. IEEE Trans. Image Proc. 19(3), 625–635 (2010)
Lellmann, J., Becker, F., Schnörr, C.: Convex optimization for multi-class image labeling with a novel family of total variation based regularizers. In: ICCV (2009)
Lellmann, J., Kappes, J., Yuan, J., Becker, F., Schnörr, C.: Convex multi-class image labeling by simplex-constrained total variation. In: Tai, X.-C., Mørken, K., Lysaker, M., Lie, K.-A. (eds.) SSVM 2009. LNCS, vol. 5567, pp. 150–162. Springer, Heidelberg (2009)
Pock, T., Chambolle, A., Cremers, D., Bischof, H.: A convex relaxation approach for computing minimal partitions. In: CVPR, pp. 810–817 (2009)
Sontag, D.A.: Approximate Inference in Graphical Models using LP Relaxations. Ph.D. thesis, Massachusetts Institute of Technology (2010)
Sundaramoorthi, G., Mennucci, A., Soatto, S., Yezzi, A.: A new geometric metric in the space of curves, and applications to tracking deforming objects by prediction and filtering. SIAM Journal on Imaging Sciences 4(1), 109–145 (2011)
Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Foundations and Trends in Machine Learning 1(1-2), 1–305 (2008)
Younes, L., Michor, P., Shah, J., Mumford, D.: A metric on shape space with explicit geodesics. Rend. Lincei Mat. Appl. 9, 25–57 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schmitzer, B., Schnörr, C. (2012). Weakly Convex Coupling Continuous Cuts and Shape Priors. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_36
Download citation
DOI: https://doi.org/10.1007/978-3-642-24785-9_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24784-2
Online ISBN: 978-3-642-24785-9
eBook Packages: Computer ScienceComputer Science (R0)