Abstract
Methods based on pairwise similarity relations have been successfully applied to unsupervised image segmentation problems. One major drawback of such approaches is their computational demand which scales quadratically with the number of pixels. Adaptations to increase the efficiency have been presented, but the quality of the results obtained with those techniques tends to decrease. The contribution of this work is to address this tradeoff for a recent convex relaxation approach for image partitioning. We propose a combination of two techniques that results in a method which is both efficient and yields robust segmentations. The main idea is to use a probabilistic sampling method in a first step to obtain a fast segmentation of the image by approximating the solution of the convex relaxation. Repeating this process several times for different samplings, we obtain multiple different partitionings of the same image. In the second step we combine these segmentations by using a meta-clustering algorithm, which gives a robust final result that does not critically depend on the selected sample points.
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
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. PAMI 22(8), 888–905 (2000)
Hofmann, T., Puzicha, J., Buhmann, J.M.: Unsupervised texture segmentation in a deterministic annealing framework. IEEE Trans. PAMI 20(8), 803–818 (1998)
Shental, N., Zomet, A., Hertz, T., Weiss, Y.: Learning and inferring image segmentations using the GBP typical cut algorithm. In: Proc. ICCV, pp. 1243–1250. IEEE Comp. Soc., Los Alamitos (2003)
Keuchel, J., Schnörr, C., Schellewald, C., Cremers, D.: Binary partitioning, perceptual grouping, and restoration with semidefinite programming. IEEE Trans. PAMI 25(11), 1364–1379 (2003)
Fowlkes, C., Belongie, S., Chung, F., Malik, J.: Spectral grouping using the Nyström method. IEEE Trans. PAMI 26(2), 214–225 (2004)
Sarkar, S., Soundararajan, P.: Supervised learning of large perceptual organization: Graph spectral partitioning and learning automata. IEEE Trans. PAMI 22(5), 504–525 (2000)
Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE T-PAMI 15(11), 1101–1113 (1993)
Wang, S., Siskind, J.M.: Image segmentation with ratio cut. IEEE Trans. PAMI 25(6), 675–690 (2003)
Williams, C.K.I., Seeger, M.: Using the Nyström method to speed up kernel machines. NIPS 13, 682–688 (2001)
Drineas, P., Mahoney, M.: On the Nyström method for approximating a Gram matrix for improved kernel-based learning. J. Mach. Learning Res. 6, 2153–2175 (2005)
Keuchel, J., Schnörr, C.: Efficient graph cuts for unsupervised image segmentation using probabilistic sampling and SVD-based approximation. In: 3rd International Workshop on Statistical and Computational Theories of Vision, Nice, France (2003)
Arora, S., Hazan, E., Kale, S.: Fast algorithms for approximate semidefinite programming using the multiplicative weights update method. In: Proc. FOCS, pp. 339–348 (2005)
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. J. Mach. Learning Res. 3, 583–617 (2003)
Fischer, B., Buhmann, J.M.: Bagging for path-based clustering. IEEE Trans. PAMI 25(11), 1411–1415 (2003)
Lange, T., Buhmann, J.M.: Combining partitions by probabilistic label aggregation. In: Proc. KDD, pp. 147–156 (2005)
Fred, A.L., Jain, A.K.: Data clustering using evidence accumulation. ICPR (4), 276–280 (2002)
de Klerk, E.: Aspects of Semidefinite Programming. Kluwer Academic Pub., Dordrecht (2002)
Goemans, M.X., Williamson, D.P.: Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming. Journal of the ACM 42(6), 1115–1145 (1995)
Frieze, A.M., Kannan, R., Vempala, S.: Fast Monte-Carlo algorithms for finding low-rank approximations. In: Proc. on FOCS, pp. 370–378 (1998)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. ICCV, pp. 416–423. IEEE Comp. Soc., Los Alamitos (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Keuchel, J., Küttel, D. (2006). Efficient Combination of Probabilistic Sampling Approximations for Robust Image Segmentation. In: Franke, K., Müller, KR., Nickolay, B., Schäfer, R. (eds) Pattern Recognition. DAGM 2006. Lecture Notes in Computer Science, vol 4174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861898_5
Download citation
DOI: https://doi.org/10.1007/11861898_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44412-1
Online ISBN: 978-3-540-44414-5
eBook Packages: Computer ScienceComputer Science (R0)