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Margin Losses for Training Conditional Random Fields

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Abstract

Structural models are shown to be highly effective tools in computer vision and image processing. Conditional random fields (CRFs) are a powerful group of statistical graphical models in the field of structured prediction. Different learning methods are used to train the CRFs. In this paper, a novel class of losses is proposed for the purpose of CRF learning. The proposed losses are categorized into marginal-based loss groups which have a different view comparing to likelihood-based training losses. The proposed losses are compared with existing losses and their performances are evaluated through three experiments. The results indicate superior efficiency of the proposed framework.

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References

  1. Ahmadi, E., Azimifar, Z., Fieguth, P., Ayatollahi, S.: Discriminative graphical model for porous media image synthesis. Iran. J. Sci. Technol. 38, 177–189 (2014)

    Google Scholar 

  2. Besag, J.: Statistical analysis of non-lattice data. Stat. 24, 179–195 (1975)

    Google Scholar 

  3. Cramer, H.: Mathematical Methods of Statistics. Princeton University Press, Princeton (1999)

    MATH  Google Scholar 

  4. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge (2000)

    Book  MATH  Google Scholar 

  5. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)

  6. Domke, J.: Learning graphical model parameters with approximate marginal inference. IEEE Trans. Pattern Anal. Mach. Intell. 35(10), 2454–2467 (2013)

    Article  Google Scholar 

  7. Hazan, T., Shashua, A.: Norm-product belief propagation: primal-dual message-passing for approximate inference. IEEE Trans. Inform. Theory 56(12), 6294–6316 (2010)

    Article  MathSciNet  Google Scholar 

  8. Jordan, A.: On discriminative vs. generative classifiers: a comparison of logistic regression and naive Bayes. Adv. Neural Inform. Process. Syst. 14, 841 (2002)

    Google Scholar 

  9. Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images. NIPS (2003)

  10. Lafferty, J., McCallum, A., Pereira, F.C.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML, pp. 282–289 (2001)

  11. Levin, A., Weiss, Y.: Learning to combine bottom-up and top-down segmentation. Int. J. Comput. Vis. 81(1), 105–118 (2009)

    Article  Google Scholar 

  12. Marroquin, J., Mitter, S., Poggio, T.: Probabilistic solution of ill-posed problems in computational vision. J. Am. Stat. Assoc. 82(397), 76–89 (1987)

    Article  MATH  Google Scholar 

  13. Masnadi-Shirazi, H., Vasconcelos, N.: On the design of loss functions for classification: theory, robustness to outliers, and savageboost. In: Advances in Neural Information Processing Systems, pp. 1049–1056 (2009)

  14. Masnadi-Shirazi, H., Vasconcelos, N.: Variable margin losses for classifier design. In: Advances in Neural Information Processing Systems, pp. 1576–1584 (2010)

  15. Nowozin, S., Gehler, P.V., Lampert, C.H.: On parameter learning in crf-based approaches to object class image segmentation. In: Computer Vision–ECCV 2010, Springer, pp. 98–111 (2010)

  16. Nowozin, S., Lampert, C.H.: Structured learning and prediction in computer vision. Founda. Trends Comput. Graph. Vis. 6(3–4), 185–365 (2011)

    MATH  Google Scholar 

  17. PVS, A., Karthik, G.: Part-of-speech tagging and chunking using conditional random fields and transformation based learning. Shallow Parsing for South Asian Languages 21 (2007)

  18. Savage, L.J.: Elicitation of personal probabilities and expectations. J. Am. Stat. Assoc. 66(336), 783–801 (1971)

    Article  MathSciNet  MATH  Google Scholar 

  19. Shafiee, M., Azimifar, Z., Fieguth, P.: Model-based tracking: Temporal conditional random fields. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 4645–4648 (2010)

  20. Sutton, C., McCallum, A.: An introduction to conditional random fields. Mach. Learn. 4(4), 267–373 (2011)

    Article  MATH  Google Scholar 

  21. Torralba, A., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. Adv. Neural Inform. Process. Syst. 17, 1401–1408 (2004)

    Google Scholar 

  22. Toyoda, T., Hasegawa, O.: Random field model for integration of local information and global information. IEEE Trans. Pattern Anal. Mach. Intell. 30(8), 1483–1489 (2008)

    Article  Google Scholar 

  23. Vishwanathan, S., Schraudolph, N.N., Schmidt, M.W., Murphy, K.P.: Accelerated training of conditional random fields with stochastic gradient methods. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 969–976, ACM (2006)

  24. Wainwright, M .J., J, T.S., Willsky, A.S.: A new class of upper bounds on the log partition function. IEEE Trans. Inform. Theory 51(7), 2313–2335 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  25. Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1(1–2), 1–305 (2008)

    MATH  Google Scholar 

  26. Wang, C., Komodakis, N., Paragios, N.: Markov random field modeling, inference & learning in computer vision & image understanding: a survey. Comput. Vis. Image Underst. 117(11), 1610–1627 (2013)

  27. Wang, S., Xu, R., Liu, B., Gui, L., Zhou, Y.: Financial named entity recognition based on conditional random fields and information entropy. In: 2014 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 838–843 (2014)

  28. Yu, D., Deng, L.: Hidden Markov models and the variants. In: Automatic Speech Recognition, Springer, pp. 23–54 (2015)

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Authors and Affiliations

  1. Department of Computer Science, Engineering and IT, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

    Ehsan Ahmadi & Zohreh Azimifar

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  1. Ehsan Ahmadi
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  2. Zohreh Azimifar
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Correspondence to Zohreh Azimifar.

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Ahmadi, E., Azimifar, Z. Margin Losses for Training Conditional Random Fields. J Math Imaging Vis 56, 499–510 (2016). https://doi.org/10.1007/s10851-016-0651-y

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  • DOI: https://doi.org/10.1007/s10851-016-0651-y

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