Abstract
Image segmentation is one of the key problems in computer vision. Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In this paper, we propose a hierarchical region-based approach to the GRF. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a far more natural model in segmenting real images. By deliberately oversegmenting at the finer scales, the method proceeds conservatively by avoiding the construction of regions which straddle a region boundary. In addition to the expected benefit of computational speed and preserved modelling elegance, our approach does not require a stopping criterion, common in iterated segmentation methods, since the hierarchy seeks the unique minimum of the original GRF model.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Angulo, J., Serra, J.: Color segmentation by ordered mergings. In: IEEE ICIP, Barcelona, September 2003, vol. 2, pp. 125–128 (2003)
Barbu, A., Zhu, S.C.: Graph Partition by Swendsen-Wang Cut. IEEE Trans. on Pattern Analysis and Machine Intelligence (2004) (under review)
Kato, Z., Berthod, M., Zeroubia, J.: A Hierarchical Markov Random Field Model and Multitemperature Annealing for Parallel Image Classification. Graphical Models and Image Processing 58(1), 18–37 (1996)
Fieguth, P., Wesolkowski, S.: Highlight and Shading Invariant Color Image Segmentation Using Simulated Annealing. In: Energy Minimization Methods in Computer Vision and Pattern Recognition III, Sophia-Antipolis, France, September 2001, pp. 314–327 (2001)
Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans-PAMI 6(6) (1984)
Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, vol. 1. Addison- Welsey, Reading (1992)
Li, S.Z.: Markov Random Field Modelling in Image Analysis. Springer, Japan (2001)
Lucchese, L., Mitra, S.K.: Color Image Segmentation: A State-of-the-Art Survey. In: Proc. of the Indian National Science Academy (INSA-A), New Delhi, India, March 2001, vol. 67 A(2), pp. 207–221 (2001)
Swendsen, R.H., Wang, J.S.: Nonuniversal critical dynamics in Monte Carlo simulations. Physical Review Letters 58(2), 86–88 (1987)
Tremeau, A., Borel, N.: A Region Growing and Merging Algorithm to Color Segmentation. Pattern Recognition 30(7), 1191–1203 (1997)
Winkler, G.: Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer, Berlin (1995)
Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(9), 884–900 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wesolkowski, S., Fieguth, P. (2004). Hierarchical Regions for Image Segmentation. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-540-30125-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23223-0
Online ISBN: 978-3-540-30125-7
eBook Packages: Springer Book Archive