Abstract
We present a discriminative method to classify data that have interdependencies in 2-D lattice. Although both Markov Random Fields (MRFs) and Conditional Random Fields (CRFs) are well-known methods for modeling such dependencies, they are often ineffective and inefficient, respectively. This is because many of the simplifying assumptions that underlie the MRF’s efficiency compromise its accuracy. As CRFs are discriminative, they are typically more accurate than the generative MRFs. This also means their learning process is more expensive. This paper addresses this situation by defining and using “Decoupled Conditional Random Fields (DCRFs)”, a variant of CRFs whose learning process is more efficient as it decouples the tasks of learning potentials. Although our model is only guaranteed to approximate a CRF, our empirical results on synthetic/real datasets show that DCRF is essentially as accurate as other CRF variants, but is many times faster to train.
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Keywords
- Support Vector Machine
- Markov Random Field
- Conditional Random Field
- Sequential Minimal Optimization
- Tumor Segmentation
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References
Besag, J.: On the statistical analysis of dirty pictures. Journal of Royal Statistical Society. Series B 48(3), 259–302 (1986)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. In: ICCV, pp. 377–384 (1999)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Dietterich, T.G.: Machine-learning research: Four current directions. The AI Magazine 18(4), 97–136 (1998)
Garcia, C., Moreno, J.: Kernel based method for segmentation and modeling of magnetic resonance images. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS, vol. 3315, pp. 636–645. Springer, Heidelberg (2004)
Jordan, M.I. (ed.): Learning in Graphical Models. MIT Press, Cambridge (1999)
Kaus, M., Warfield, S., Nabavi, A., Black, P., Jolesz, F., Kikinis, R.: Automated segmentation of MR images of brain tumors. Radiology 218, 586–591 (2001)
Kindermann, R., Snell, J.: Makrov random fields and their applications. American Mathematical Society (1980)
Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images. In: NIPS (2003)
Lafferty, J., Pereira, F., McCallum, A.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML (2001)
Lee, C., Schmidt, M., Greiner, R.: Support vector random fields for spatial classification. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS, vol. 3721, pp. 121–132. Springer, Heidelberg (2005)
Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer, Tokyo (2001)
Lin, H.-T., Lin, C.-J., Weng, R.C.: A note on platt’s probabilistic outputs for support vector machine. Technical report (2003)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Platt, J.: Probabilistic outputs for support vector machines and comparison to regularized likelihood methods. MIT Press, Cambridge (2000)
Schmidt, M.: Automatic brain tumor segmentation. Master’s thesis, University of Alberta (2005)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Taskar, B., Chatalbashev, V., Koller, D.: Learning associative markov networks. In: ICML 2004, p. 102. ACM Press, New York (2004)
Torralba, A., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. In: NIPS, vol. 17. MIT Press, Cambridge (2005)
Zhang, J., Ma, K., Er, M., Chong, V.: Tumor segmentation from magnetic resonance imaging by learning via one-class support vector machine. In: International Workshop on Advanced Image Technology, pp. 207–211 (2004)
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Lee, CH., Greiner, R., Zaïane, O. (2006). Efficient Spatial Classification Using Decoupled Conditional Random Fields. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science(), vol 4213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11871637_28
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DOI: https://doi.org/10.1007/11871637_28
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