Abstract
We propose a higher order conditional random field built over a graph of superpixels for partitioning natural images into coherent segments. Our model operates at both superpixel and segment levels and includes potentials that capture similarity, proximity, curvilinear continuity and familiar configuration. For a given image, these potentials enforce consistency and regularity of labellings. The optimal one should maximally satisfy local, pairwise and global constraints imposed respectively by the learned association, interaction and higher order potentials. Experiments on a variety of natural images show that integration of higher order potentials qualitatively and quantitatively improves results and leads to more coherent and regular segments.
This is a preview of subscription content, log in via an institution to check access.
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
Zhu, S.C., Yuille, A.: Region competition: Unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18, 884–900 (1996)
Chen, Y., Tagare, H., Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K., Briggs, R., Geiser, E.: Using prior shapes in geometric active contours in a variational framework. Int. J. Comput. Vision 50, 315–328 (2002)
Kato, Z., Pong, T.C., Lee, J.C.M.: Color image segmentation and parameter estimation in a markovian framework. Pattern Recogn. Lett. 22, 309–321 (2001)
Bertelli, L., Sumengen, B., Manjunath, B., Gibou, F.: A variational framework for multiregion pairwise-similarity-based image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1400–1414 (2008)
Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int. J. Comput. Vision 72, 195–215 (2007)
Tu, Z., Zhu, S.C.: Image segmentation by data-driven markov chain monte carlo. IEEE Trans. Pattern Anal. Mach. Intell. 24, 657–673 (2002)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)
Barbu, A., Zhu, S.C.: Generalizing swendsen-wang to sampling arbitrary posterior probabilities. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1239–1253 (2005)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)
Kim, T.H., Lee, K.M., Lee, S.U.: Learning full pairwise affinities for spectral segmentation. In: IEEE CVPR, pp. 2101–2108 (2010)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, vol. 18, pp. 282–289 (2001)
Kumar, S., Hebert, M.: Discriminative random fields. Int. J. Comput. Vision 68(2), 179–201 (2006)
Ren, X., Fowlkes, C.C., Malik, J.: Figure/ground assignment in natural images. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3952, pp. 614–627. Springer, Heidelberg (2006)
He, X., Zemel, R.S., Carreira-Perpinan, M.A.: Multiscale conditional random fields for image labeling (2004)
He, X., Zemel, R.S., Ray, D.: Learning and incorporating top-down cues in image segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 338–351. Springer, Heidelberg (2006)
Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. Int. J. Comput. Vision 81, 2–23 (2009)
Ren, X., Fowlkes, C., Malik, J.: Learning probabilistic models for contour completion in natural images. Int. J. Comput. Vision 77, 47–63 (2008)
Ren, X., Malik, J.: Learning a classification model for segmentation. In: IEEE ICCV, vol. 2, pp. 10–18 (2003)
Kohli, P., Ladicky, L.U., Torr, P.H.: Robust higher order potentials for enforcing label consistency. Int. J. Comput. Vision 82, 302–324 (2009)
Brox, T., Weickert, J.: A tv flow based local scale estimate and its application to texture discrimination. J. of Visual Communication and Image Representation 17, 1053–1073 (2006)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26, 530–549 (2004)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29, 854–869 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Besbes, O., Boujemaa, N., Belhadj, Z. (2011). Embedding Gestalt Laws on Conditional Random Field for Image Segmentation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-24028-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24027-0
Online ISBN: 978-3-642-24028-7
eBook Packages: Computer ScienceComputer Science (R0)