probabilistic image processing and bayesian network
DESCRIPTION
Probabilistic image processing and Bayesian network. Kazuyuki Tanaka Graduate School of Information Sciences, Tohoku University [email protected] http://www.smapip.is.tohoku.ac.jp/~kazu/. References - PowerPoint PPT PresentationTRANSCRIPT
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RC2005 (19 July, 2005, Sendai)
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Probabilistic image processing and Bayesian networkKazuyuki Tanaka
Graduate School of Information Sciences,Tohoku University
[email protected]://www.smapip.is.tohoku.ac.jp/~kazu/
ReferencesReferencesK. Tanaka: Statistical-mechanical approach to image processing (Topical Review), J. Phys. A, vol.35, pp.R81-R150 (2002).K. Tanaka, H. Shouno, M. Okada and D. M. Titterington: Accuracy of the Bethe approximation for hyperparameter estimation in probabilistic image processing, J. Phys. A, vol.37, pp.8675-8695 (2004).
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RC2005 (19 July, 2005, Sendai)
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Bayesian Network and Belief Propagation
Probabilistic Information Processing
Probabilistic Model
Bayes Formula
Belief Propagation
J. Pearl: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, 1988).C. Berrou and A. Glavieux: Near optimum error correcting coding and decoding: Turbo-codes, IEEE Trans. Comm., 44 (1996).
Bayesian Network
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Formulation of Belief PropagationFormulation of Belief PropagationLink between Link between belief propagation belief propagation andand statistical statistical mechanics.mechanics.Y. Kabashima and D. Saad, Belief propagation vs. TAP for decoding corrupted messages, Europhys. Lett. 44 (1998). M. Opper and D. Saad (eds), Advanced Mean Field Methods ---Theory and Practice (MIT Press, 2001).
Generalized belief propagationGeneralized belief propagationJ. S. Yedidia, W. T. Freeman and Y. Weiss: Constructing free-energy approximations and generalized belief propagation algorithms, IEEE Transactions on Information Theory, 51 (2005).
Information geometrical interpretation Information geometrical interpretation of belief propagationof belief propagation
S. Ikeda, T. Tanaka and S. Amari: Stochastic reasoning, free energy, and information geometry, Neural Computation, 16 (2004).
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Application of Belief PropagationApplication of Belief PropagationImage ProcessingImage ProcessingK. Tanaka: Statistical-mechanical approach to image processing (Topical Review), J. Phys. A, 35 (2002).A. S. Willsky: Multiresolution Markov Models for Signal and Image Processing, Proceedings of IEEE, 90 (2002).
Low Density Parity Check CodesLow Density Parity Check CodesY. Kabashima and D. Saad: Statistical mechanics of low-density parity-check codes (Topical Review), J. Phys. A, 37 (2004). S. Ikeda, T. Tanaka and S. Amari: Information geometry of turbo and low-density parity-check codes, IEEE Transactions on Information Theory, 50 (2004).
CDMA Multiuser Detection AlgorithmCDMA Multiuser Detection AlgorithmY. Kabashima: A CDMA multiuser detection algorithm on the basis of belief propagation, J. Phys. A, 36 (2003).T. Tanaka and M. Okada: Approximate Belief propagation, density evolution, and statistical neurodynamics for CDMA multiuser detection, IEEE Transactions on Information Theory, 51 (2005).
SSatisfability atisfability PProblemroblemO. C. Martin, R. Monasson, R. Zecchina: Statistical mechanics methods and phase transitions in optimization problems, Theoretical Computer Science, 265 (2001).M. Mezard, G. Parisi, R. Zecchina: Analytic and algorithmic solution of random satisfability problems, Science, 297 (2002).
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RC2005 (19 July, 2005, Sendai) 5
ContentsContents1. Introduction2. Belief Propagation3. Bayesian Image Analysis and Gaussian
Graphical Model4. Image Segmentation5. Concluding Remarks
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RC2005 (19 July, 2005, Sendai)
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Formulation for approximate algorithmAccuracy of the approximate algorithm
Belief Propagation
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RC2005 (19 July, 2005, Sendai)
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Tractable Model
Factorizable
Not Factorizable
Probabilistic models with no loop are tractable.
Probabilistic models with loop are not tractable.
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RC2005 (19 July, 2005, Sendai)
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Probabilistic Model on a Graph with Loops
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RC2005 (19 July, 2005, Sendai)
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Message Passing Rule of Belief Propagation
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RC2005 (19 July, 2005, Sendai)
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Approximate Representation of Marginal Probability
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RC2005 (19 July, 2005, Sendai)
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Fixed Point Equation and Iterative Method
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RC2005 (19 July, 2005, Sendai) 12
ContentsContents1. Introduction2. Belief Propagation3. Bayesian Image Analysis and Gaussian
Graphical Model4. Image Segmentation5. Concluding Remarks
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RC2005 (19 July, 2005, Sendai)
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Bayesian Image Analysis
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Bayesian Image AnalysisDegradation Process ,, ii gf
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Bayesian Image Analysis
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Bayesian Image Analysis
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Hyperparameter Determination by Maximization of Marginal Likelihood
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Maximization of Marginal Likelihood by EM (Expectation Maximization) Algorithm
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RC2005 (19 July, 2005, Sendai)
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Maximization of Marginal Likelihood by EM (Expectation Maximization) Algorithm
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21RC2005 (19 July, 2005, Sendai)
One-Dimensional One-Dimensional SignalSignal
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22RC2005 (19 July, 2005, Sendai)
Image Restoration by Gaussian Graphical ModelImage Restoration by Gaussian Graphical Model
Original ImageOriginal Image Degraded ImageDegraded Image
MSE: 1529MSE: 1529
MSE: 1512MSE: 1512
EM Algorithm with Belief Propagation
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RC2005 (19 July, 2005, Sendai)
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Exact Results of Gaussian Graphical Model
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24RC2005 (19 July, 2005, Sendai)
Comparison of Belief Propagation with Comparison of Belief Propagation with Exact Results in Gaussian Graphical ModelExact Results in Gaussian Graphical Model
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Belief Belief PropagationPropagation 327327 0.0006110.000611 36.30236.302 -5.19201-5.19201
ExactExact 315315 0.0007590.000759 37.91937.919 -5.21444-5.21444
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Belief Belief PropagationPropagation 260260 0.0005740.000574 33.99833.998 -5.15241-5.15241
ExactExact 236236 0.0006520.000652 34.97534.975 -5.17528-5.17528
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25RC2005 (19 July, 2005, Sendai)
Image Restoration by Image Restoration by Gaussian Graphical ModelGaussian Graphical Model
Original ImageOriginal Image
MSE:315MSE:315MSE: 325MSE: 325
MSE: 545MSE: 545 MSE: 447MSE: 447MSE: 411MSE: 411
MSE: 1512MSE: 1512
Degraded ImageDegraded Image Belief PropagationBelief Propagation
Lowpass FilterLowpass Filter Median FilterMedian Filter
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Wiener Filter
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26RC2005 (19 July, 2005, Sendai)
Original ImageOriginal Image
MSE236MSE236MSE: 260MSE: 260
MSE: 372MSE: 372 MSE: 244MSE: 244MSE: 224MSE: 224
MSE: 1529MSE: 1529
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Lowpass FilterLowpass Filter Median FilterMedian Filter
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Wiener Filter
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Image Restoration by Gaussian Image Restoration by Gaussian Graphical ModelGraphical Model
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27RC2005 (19 July, 2005, Sendai)
Extension of Belief PropagationExtension of Belief Propagation
Generalized Belief PropagationGeneralized Belief PropagationJ. S. Yedidia, W. T. Freeman and Y. Weiss: Constructing free-energy J. S. Yedidia, W. T. Freeman and Y. Weiss: Constructing free-energy approximations and generalized belief propagation algorithms, IEEE approximations and generalized belief propagation algorithms, IEEE Transactions on Information Theory, Transactions on Information Theory, 5151 (2005). (2005).
Generalized belief propagation is equivalent Generalized belief propagation is equivalent to the cluster variation method in statistical to the cluster variation method in statistical mechanicsmechanicsR. Kikuchi: A theory of cooperative phenomena, Phys. Rev., R. Kikuchi: A theory of cooperative phenomena, Phys. Rev., 8181 (1951). (1951).T. Morita: Cluster variation method of cooperative phenomena and its T. Morita: Cluster variation method of cooperative phenomena and its generalization I, J. Phys. Soc. Jpn, generalization I, J. Phys. Soc. Jpn, 1212 (1957). (1957).
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28RC2005 (19 July, 2005, Sendai)
Image Restoration by Gaussian Graphical ModelImage Restoration by Gaussian Graphical Model
2ˆ||
1MSE
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Belief Belief PropagationPropagation 327327 0.0006110.000611 36.30236.302 -5.19201-5.19201
Generalized Generalized Belief Belief
PropagationPropagation315315 0.0007580.000758 37.90937.909 -5.21172-5.21172
ExactExact 315315 0.0007590.000759 37.91937.919 -5.21444-5.21444
ˆ,ˆln gP
MSEMSE
Belief Belief PropagationPropagation 260260 0.0005740.000574 33.99833.998 -5.15241-5.15241
Generalized Generalized Belief Belief
PropagationPropagation236236 0.0006520.000652 34.97134.971 -5.17256-5.17256
ExactExact 236236 0.0006520.000652 34.97534.975 -5.17528-5.17528
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29RC2005 (19 July, 2005, Sendai)
Image Restoration by Gaussian Graphical Image Restoration by Gaussian Graphical Model and Conventional FiltersModel and Conventional Filters
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Belief Belief PropagationPropagation 327327 Lowpass Lowpass
FilterFilter(3x3)(3x3) 388388
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30RC2005 (19 July, 2005, Sendai)
Image Restoration by Gaussian Graphical Image Restoration by Gaussian Graphical Model and Conventional FiltersModel and Conventional Filters
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Belief Belief PropagationPropagation 260260 Lowpass Lowpass
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RC2005 (19 July, 2005, Sendai) 31
ContentsContents1. Introduction2. Belief Propagation3. Bayesian Image Analysis and Gaussian
Graphical Model4. Image Segmentation5. Concluding Remarks
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RC2005 (19 July, 2005, Sendai) 32
Image Segmentation by Image Segmentation by Gauss Mixture ModelGauss Mixture Model
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RC2005 (19 July, 2005, Sendai) 33
Image Segmentation by Combining Image Segmentation by Combining Gauss Mixture Model with Potts Model Gauss Mixture Model with Potts Model
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34RC2005 (19 July, 2005, Sendai)
Image SegmentationImage Segmentation
Original Image Histogram Gauss Mixture Model
Gauss Mixture Model and Potts Model
Belief Belief PropagationPropagation
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35RC2005 (19 July, 2005, Sendai)
Motion DetectionMotion Detection
SegmentationAND
Detection
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RC2005 (19 July, 2005, Sendai) 36
ContentsContents1. Introduction2. Belief Propagation3. Bayesian Image Analysis and Gaussian
Graphical Model4. Image Segmentation5. Concluding Remarks
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37RC2005 (19 July, 2005, Sendai)
SummarySummaryFormulation of belief propagationFormulation of belief propagationAccuracy of belief propagation in Bayesian Accuracy of belief propagation in Bayesian image analysis by means of Gaussian image analysis by means of Gaussian graphical model (Comparison between the graphical model (Comparison between the belief propagation and exact calculation)belief propagation and exact calculation)Some applications of Bayesian image Some applications of Bayesian image analysis and belief propagationanalysis and belief propagation
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38RC2005 (19 July, 2005, Sendai)
Related ProblemRelated Problem
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H. Nishimori: Statistical Physics of Spin Glasses and Information Processing: An Introduction, Oxford University Press, Oxford, 2001.
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RC2005 (19 July, 2005, Sendai)RC2005 (19 July, 2005, Sendai) 3939
確率的情報処理の動向確率的情報処理の動向田中和之・樺島祥介編著 , “ ミニ特集 / ベイズ統計・統計力学と情報処理” , 計測と制御 2003 年 8 月号.田中和之,田中利幸,渡辺治 他著,“連載 / 確率的情報処理と統計力学 ~様々なアプローチとそのチュートリアル~”,数理科学 2004 年 11 月号から開始.田中和之,岡田真人,堀口剛 他著,“小特集 / 確率を手なづける秘伝の計算技法 ~古くて新しい確率・統計モデルのパラダイム~”,電子情報通信学会誌 2005 年 9 月号