18 October, 2013 MVA ENS Cachan. Lecture 6: Introduction to graphical models Iasonas Kokkinos

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1 Machine Learning for Computer Vision 1 18 October, 2013 MVA ENS Cachan Lecture 6: Introduction to graphical models Iasonas Kokkinos Iasonas.kokkinos@ecp.fr Center for Visual Computing Ecole Centrale Paris Galen Group INRIA-Saclay

2 2 Lecture outline Introduction Graphs and computation Graphical models Chain-structured graphical models Tree-structured graphical models

3 Problem 1: measuring the length of a queue 3 while only complaining to your closest neighbors

4 Problem 2: two queues 4

5 Problem N: N queues 5

6 6 Lecture outline Introduction Computation on graphs Graphical models Chain-structured graphical models Tree-structured graphical models

7 Graphical models 7 Blend of graph theory and probability Graph nodes: random variables Graph edges: relationships Directed graphs: Bayesian Networks Undirected graphs: Markov Random Fields

8 Bayesian Networks 8 Directed Acyclic Graph Nodes - : random variables Edges - : dependencies Leave from `parents, arrive at children Distribution of each child conditioned on parent: Joint probability distribution: Bayesian network for Brownian motion:

9 Conditional independencies 9 We can trivially obtain a bayesian network for a probability distribution Fully connected graph! Bayesian net Non-trivial cases: some conditional independence Main interest: exploit independencies for training & inference

10 A Bayesian Network-based Expert System 10 `Asia Network A: trip to Asia T: turbeculosis S: smoking L: Lung Cancer B: Bronchitis E: Turbeculosis/Lung Cancer X-ray results D: Dyspnea Given: X-rays, Dyspnea, patient went to Asia, patient smokes Wanted: posterior probability of Bronchitis

11 Reading Conditional Independence from a BN Graph 11 Big Ben Clock 1 Clock 2 State 1 State 2 State 3 Bicycle Motorcycle Cycle `Explaining away

12 12 Lecture outline Introduction Chain-structured graphical models Hidden Markov Models Kalman Filter Tree-structured graphical models

13 Hidden variables 13 Mixture models: combine simple distributions into complex one Mixture Models Data clustering: side-product of parameter estimation

14 Image clustering - segmentation 14 Observations are not independent (Consecutive points: common region)

15 Dependent Hidden variables 15 Mixture Models Hiden Markov Models Introduce dependencies between hidden variables First-order Markov Model M x M transition matrix

16 Hidden Markov Models 16 hidden states Dependent sequence of observations observed process Conditioned on state sequence, observations become independent Parameters Observation probabilities discrete continuous Transition probabilities Starting probabilities Estimation: Expectation Maximization

17 HMMs & Speech Recognition 17 Input: Air pressure signal Output: words/phrases Probabilistic model: signal given word Hidden Variables: phonemes (/ae/,/ey/,/z/ ) phone subunits Hidden variables: not independent Show,shoe,she,sell,sea, Sv? (Sven,svelte,..) Sb?

18 18 Lecture outline Introduction Chain-structured graphical models Hidden Markov Models Kalman Filter Tree-structured graphical models Loopy graphical models

19 Detection vs. Tracking 19 t=1 t=2 t=20 t=21

20 Detection vs. Tracking 20 t=1 t=2 t=20 t=21 Detection: each frame independently

21 Detection vs. Tracking 21 t=1 t=2 t=20 t=21 Tracking with dynamics: combine image measurements with our expectation of the object s motion. Restrict search for the object Measurement noise is reduced by trajectory smoothness.

22 Tracking 22 Input: Series of Images Output: Estimate of object s motion/action/gesture Hidden Variables (`State ) Time-dependent Assume linear dynamics State, X Observations, Y

23 Simple Linear Dynamics 23 Brownian Motion Constant Velocity Time Time Slide credit: E. Sudderth

24 Linear Dynamical Systems 24 Noise in State, noise in Observations hidden states observed process Slide credit: E. Sudderth

25 Inference Tasks 25 Prediction Filtering (Kalman Filter) Smoothing Slide credit: E. Sudderth

26 Lecture outline 26 Introduction Chain-structured graphical models Tree-structured graphical models Pictorial Structures Max/Sum-Product algorithm

27 Problem N: N queues 27

28 28 Machine Learning for Computer Vision Lecture 6 Part-based Models x1 x2 x3 x4 x5

29 29 Machine Learning for Computer Vision Lecture 6 Part-based Models x1 x2 x3 x4 x5

30 Unary terms (appearance model) 30 Consider a patch of the image around the location of the part Construct statistical model for appearance at part location Filterbank responses SIFT features

31 Components of a part-based model: structure Pairwise terms (deformation model) 31 Deformable objects as physical systems Masses: driven towards good image locations Springs: enforce relations among parts Star models (tree-structured models) Pick single part as root (e.g. the nose) Model other parts (eyes, mouth corners) w.r.t. root Probability of configuration Unary Terms Pairwise Terms How can we maximize over L?

32 32 Machine Learning for Computer Vision Lecture 6 Deformable Part Models (DPMs) S(x) = P X x1 x2 x3 Up (xp ) + Bp (x, xp ) p=1 x5 x4 Local appearance Up (xp ) = hwp, H(xp )i Pairwise compatibility Bp (x, xp ) = Object score P X S(x) = p=1 (h hp h p )2 (v 0 0 max [U (x ) + B (x, x )] p p 0 x vp v p )2

33 Lecture outline 33 Introduction Chain-structured graphical models Tree-structured graphical models Pictorial Structures Max/Sum-Product algorithm

34 Inference on tree-structured Graphs 34 Assume we want to find the most likely value of Brute-force maximization: Exploit factorization

35 Exploiting the factorization 35

36 Exploiting the factorization 36

37 Max-Product algorithm 37 Distributed computation on a graph `message-passing Message from node i to node j `Given all I know from the rest, this is where you should be Beliefs: At convergence

38 Sum-Product algorithm 38 Distributed computation on a graph `message-passing Message from node i to node j `Given all I know from the rest, this is where you should be Beliefs: At convergence

39 Max/Sum-Product algorithms 39 Asychronous inference Local computations Guaranteed optimality for tree-structured graphs Equivalent algorithms: Tracking: Kalman Filtering (Sum-Product) HMMs: Alpha-Beta (Sum-Product) Coding/HMMs: Viterbi Algorithm (Max-Product) Bayesian Nets: Belief Propagation (Sum-Product) Max Product: Dynamic Programming

40 40 Machine Learning for Computer Vision Lecture 6 Deformable Part Models (DPMs) S(x) = P X x1 x2 x3 Up (xp ) + Bp (x, xp ) p=1 x5 x4 Local appearance Up (xp ) = hwp, H(xp )i Pairwise compatibility Bp (x, xp ) = Object score P X S(x) = p=1 (h hp h p )2 (v 0 0 max [U (x ) + B (x, x )] p p 0 x vp v p )2

41 Pictorial Structures 41 Graphical model Nodes: part locations Edges: dependencies - relative locations Object localization: maximization with respect to Unary terms Pairwise terms Message Passing: Efficient Implementation: Felzenszwalb & Huttenlocher, CVPR 2001/IJCV 05

42 42

43 Generalized Distance Transforms- 1D 43 Plot min x1 {m 1 (x 1 )+ (x 1,x 2 )} as function of x 2 (x 1 = a, x 2 ) = 2 (x 2 a) 2 2 (x 2 b) 2 m 1 (x 1 = a) a b m 1 (x 1 = b) x 1 x 2

44 GDT- 1D 44 Plot min x1 {m 1 (x 1 )+ (x 1,x 2 )} as function of x 2 x 1 x 2 For each x 2 Finding min over x 1 is equivalent finding minimum over set of offset parabolas Lower envelope computed in O(h) rather than O(h 2 ) via gen. distance transform

45 GDT- 1D 45 Plot min x1 {m 1 (x 1 )+ (x 1,x 2 )} as function of x 2 x 1 x 2 For each x 2 Finding min over x 1 is equivalent finding minimum over set of offset parabolas Lower envelope computed in O(h) rather than O(h 2 ) via gen. distance transform

46 Detection Results 46

47 Part-based models + discriminative framework 47 P. Felzenszwalb, D. McAllester, D. Ramanan`A Discriminatively Trained, Multiscale, Deformable Part Model CVPR 2008 Linear classifier gives individual part cost Gaussian-like cost for spatial offsets Learn cost for parts and deformations Discriminative framework State-of-the-art, 2008

48 Discriminative Training of DPMs Training data consists of images with labeled bounding boxes. Need to learn the model structure, filters and deformation costs. 48 Training

49 Multiple Instance Learning 49 Typical Learning Multiple Instance Learning Positive bag: at least one instance should be positive Negative bag: no instance should be positive Slide Credit: K. Grauman

50 50 Score function for DPM detection data term spatial prior score(p 0,...,p n )= n F i (H, p i ) n d i (dx 2 i,dy 2 i ) i=0 i=1 displacements filters deformation parameters score(z) = (H, z) concatenation filters and deformation parameters concatenation of HOG features and part displacement features

51 51 SVM training for DPMs Classifiers that score an example x using f (x) = max z Z(x) (x, z) β are model parameters z are latent values Training data D =(x 1,y 1,..., x n,y n ) y i { 1, 1} We would like to find β such that: y i f (x i ) > 0 Minimize L D ( )= C n i=1 max(0, 1 y i f (x i ))

52 52 Reduce to Latent SVM training problem Annotation & Latent Variables Positive example specifies some z should have high score Bounding box defines range of root locations - Parts can be anywhere - This defines Z(x) P.F. Felzenszwalb, R. B. Girshick, D. Ramanan and D. A. McAllester, a discriminatively trained multiscale deformable part model, IEEE PAMI, 2010

53 53 Latent SVM training L D ( )= C n i=1 max(0, 1 y i f (x i )) Convex if we fix z for positive examples Optimization: - Initialize β and iterate: - Pick best z for each positive example - Optimize β via gradient descent with data-mining

54 Appendix: Inference & Learning for Chain-structured GMs 54 Discrete State: Hidden Markov Models Forward-Backward Viterbi Learning Continuous State Kalman Filter Particle Filters

55 Hidden Markov Models 55 hidden states observed process Dynamics take simple form based on hidden states Process: word utterance Hidden States: phonemes Observed: speech signal Mixture Models HMMs

56 Three Main problems 56 Likelihood Evaluation Inference Parameter Estimation

57 Three Algorithms 57 Likelihood Evaluation (Forward-Backward) Inference (Viterbi) Parameter Estimation (EM/Baum-Welch)

58 Likelihood Evaluation 58 Using Marginalization Complexity: where N is the number of states

59 Exploiting the independencies 59 Rewrite likelihood as Introduce Observe that

60 Recursion for α 60

61 Recursion for α 61 Forward in time

62 Rewrite likelihood 62 Gain in computation: Brute force: Recursive:

63 Backward Variables β 63 Similar recursion holds: Backward in time:

64 64 Discrete State: Hidden Markov Models Forward-Backward Viterbi Learning Continuous State Kalman Filter Particle Filters

65 Three Main problems 65 Likelihood Evaluation (Forward-backward) Inference (Viterbi) Parameter Estimation (EM)

66 Posterior for a single state 66 Bayes optimal decision.. Introduce Based on Bayes rule Can rewrite using α,β

67 γ=αβ 67 So

68 Optimal State Sequence 68 Different from concatenation of optimal states Viterbi Algorithm Exploit Decomposable cost function Dynamic Programming

69 69 Exploit conditional independence

70 Viterbi Algorithm: recursion 70 Introduce Express recursively:

71 Viterbi Algorithm 71 Initialize Recurse Finalize Backtrack

72 72 Discrete State: Hidden Markov Models Forward-Backward Viterbi Learning Continuous State Kalman Filter Particle Filters

73 Three Main problems 73 Likelihood Evaluation (Forward-backward) Inference (Viterbi) Parameter Estimation (EM)

74 Full observation log-likelihood 74 Hidden Variables: State Sequence Full observations: Observations + HVs. Full observation likelihood Full Observation log-likelihood:

75 Parameter Estimates for Full Observations 75

76 Expectation Maximization 76 E-step: Expectation of Full-observation log-likelihood M-step: Maximization w.r.t parameters

77 E-step 77 Use current parameter estimate Run forward-backward algorithm Form γ Plug in full-observation log-likelihood

78 Posterior on joint states 78

79 M-step 79 Replace hidden variables with their expectations

80 5 th Lecture Layout 80 Discrete State: Hidden Markov Models Forward-Backward Viterbi Learning Continuous State Kalman Filter Particle Filters

81 Linear Dynamical Systems 81 Noise in State, noise in Observations hidden states observed process Identical dependencies among random variables But now integrals instead of summations All distributions are Gaussian

82 Kalman Filter Recursion 82 State estimate As in HMMs, come up with a recursion State estimate = prediction x correction All distributions are Gaussian: analytic expressions

83 Prediction distribution 83 Write in terms of previous state posterior

84 Posterior distribution 84 Posterior distribution Consider joint distribution of state and observation

85 Joint distribution of state and observation 85 We have distribution for x, need to consider y So

86 Posterior distribution on state 86 Condition on new observation y

87 1D Discrete-time Brownian motion 87 Dynamical model: Deterministic system driven by random excitation Input: White Gaussian Noise Output: Brownian motion Realization of input: Realization of output:

88 Example: Randomly Drifting Points 88

89 2D Brownian motion as a model for natural images 89 2D White Gaussian Noise 2D BM (stochastic fractal)

90 Generative model for Brownian motion 90 Synthesis equation: Coefficients Basis 2D: Fourier Synthesis Equation: use sinusoidals as signal basis PCA: `Customized basis for stochastic process (Karhunen-Loeve) PCA on Brownian motion: Sinusoidal basis

91 Random Variable Dependencies 91 Stochastic process: sequence of RVs Independent RVs (WGN) Dependent RVs (Brownian Motion) (1 st -order Markov) 2D:

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