Abstract
The goal of segmentation is to partition an image into a finite set of regions, homogeneous in some (e.g., statistical) sense, thus being an intrinsically discrete problem. Bayesian approaches to segmentation use priors to impose spatial coherence; the discrete nature of segmentation demands priors defined on discrete-valued fields, thus leading to difficult combinatorial problems.
This paper presents a formulation which allows using continuous priors, namely Gaussian fields, for image segmentation. Our approach completely avoids the combinatorial nature of standard Bayesian approaches to segmentation. Moreover, it’s completely general, i.e., it can be used in supervised, unsupervised, or semi-supervised modes, with any probabilistic observation model (intensity, multispectral, or texture features).
To use continuous priors for image segmentation, we adopt a formulation which is common in Bayesian machine learning: introduction of hidden fields to which the region labels are probabilistically related. Since these hidden fields are real-valued, we can adopt any type of spatial prior for continuous-valued fields, such as Gaussian priors. We show how, under this model, Bayesian MAP segmentation is carried out by a (generalized) EM algorithm. Experiments on synthetic and real data shows that the proposed approach performs very well at a low computational cost.
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References
Balram, N., Moura, J.: Noncausal Gauss-Markov random fields: parameter structure and estimation. IEEE Trans. Information Theory 39, 1333–1355 (1993)
Bernardo, J., Smith, A.: Bayesian Theory. J. Wiley & Sons, Chichester (1994)
Böhning, D.: Multinomial logistic regression algorithm. Annals Inst. Stat. Math. 44, 197–200 (1992)
Böhning, D., Lindsay, B.: Monotonicity of quadratic-approximation algorithms. Annals Inst. Stat. Math. 40, 641–663 (1988)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Patt. Anal. Mach. Intell. 23, 1222–1239 (2001)
Chung, F.: Spectral Graph Theory. American Mathematical Society, Providence (1997)
Cross, G., Jain, A.: Markov random field texture models. IEEE Trans. Patt. Anal. and Mach. Intell. 5, 25–39 (1983)
Derin, H., Elliot, H.: Modelling and segmentation of noisy and textured images in Gibbsian random fields. IEEE Trans. Patt. Anal. and Mach. Intell. 9, 39–55 (1987)
Figueiredo, M.: Bayesian image segmentation using wavelet-based priors. In: Proc. of IEEE CVPR 2005, San Diego, CA (2005)
Figueiredo, M., Jain, A.K.: Unsupervised learning of finite mixture models. IEEE Trans. Patt. Anal. and Mach. Intell. 24, 381–396 (2002)
Haralick, R., Shanmugan, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst., Man, and Cybernetics 8, 610–621 (1973)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning. Springer, New York (2001)
Hermes, L., Buhmann, J.: A minimum entropy approach to adaptive image polygonization. IEEE Trans. Image Proc. 12, 1243–1258 (2003)
Hofmann, T., Puzicha, J., Buhmann, J.: Unsupervised texture segmentation in a deterministic annealing framework. IEEE Trans. Patt. Anal. and Mach. Intell. 20, 803–818 (1998)
Jain, A.: Fundamentals of Digital Image Processing. Prentice Hall, Englewood Cliffs (1989)
Jain, A., Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognition 24, 1167–1186 (1991)
Kim, J., Fisher, J., Yezzi, A., Çetin, M., Willsky, A.: A nonparametric statistical method for image segmentation using information theory and curve evolution. IEEE Trans. Image Proc. (2005) (to appear)
Krishnapuram, B., Carin, L., Figueiredo, M., Hartemink, A.: Learning sparse Bayesian classifiers: multi-class formulation, fast algorithms, and generalization bounds. IEEE-TPAMI 27(6) (2005)
Lange, K., Hunter, D., Yang, I.: Optimization transfer using surrogate objective functions. Jour. Comp. Graph. Stat. 9, 1–59 (2000)
Li, S.Z.: Markov Random Field Modelling in Computer Vision. Springer, Heidelberg (2001)
Magnus, J., Neudecker, H.: Matrix Differential Calculus. John Wiley & Sons, Chichester (1988)
Marroquin, J., Santana, E., Botello, S.: Hidden Markov measure field models for image segmentation. IEEE Trans. Patt. Anal. and Mach. Intell. 25, 1380–1387 (2003)
McLachlan, G., Krishnan, T.: The EM Algorithm and Extensions. John Wiley & Sons, New York (1997)
Nowak, R., Figueiredo, M.: Unsupervised progressive parsing of Poisson fields using minimum description length criteria. In: Proc. IEEE ICIP 1999, Kobe, Japan, vol. II, pp. 26–29 (1999)
Randen, T., Husoy, J.: Filtering for texture classification: a comparative study. IEEE Trans. Patt. Anal. Mach. Intell. 21, 291–310 (1999)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color and texture cues. IEEE Trans. Patt. Anal. Mach. Intell. 26, 530–549 (2004)
Sharon, E., Brandt, A., Basri, R.: Segmentation and boundary detection using multiscale intensity measurements. In: Proc. IEEE CVPR, Kauai, Hawaii, vol. I, pp. 469–476 (2001)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 22, 888–905 (2000)
Unser, M.: Texture classification and segmentation using wavelet frames. IEEE Trans. Image Proc. 4, 1549–1560 (1995)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: Proc. Intern. Conf. on Computer Vision – ICCV 1999, pp. 975–982 (1999)
Williams, C., Barber, D.: Bayesian classification with Gaussian priors. IEEE Trans. Patt. Anal. and Mach. Intell. 20, 1342–1351 (1998)
Wu, Z., Leahy, R.: Optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 15, 1101–1113 (1993)
Zabih, R., Kolmogorov, V.: Spatially coherent clustering with graph cuts. In: Proc. IEEE-CVPR, vol. II, pp. 437–444 (2004)
Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and Bayes/MDL for multiband image segmentation. IEEE Trans. Patt. Anal. Mach. Intell. 18, 884–900 (1996)
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Figueiredo, M.A.T. (2005). Bayesian Image Segmentation Using Gaussian Field Priors. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_6
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DOI: https://doi.org/10.1007/11585978_6
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