Robert Collins CSE586 CSE 586, Spring 2015 Computer Vision II

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1 CSE 586, Spring 2015 Computer Vision II Hidden Markov Model and Kalman Filter

2 Recall: Modeling Time Series State-Space Model: You have a Markov chain of latent (unobserved) states Each state generates an observation x 1! x 2! x n-1! x n! x n+1! o 1! o 2! o n-1! o n! o n+1!

3 Modeling Time Series P(x1,x2,x3,x4,...,o1,o2,o3,o4,...) = P(x1)P(o1 x1)p(x2 x1)p(o2 x2)p(x3 x2)p(o3 x3)p(x4 x3)p(o4 x4)... x 1! x 2! x n-1! x n! x n+1! o 1! o 2! o n-1! o n! o n+1!

4 Modeling Time Series Examples of State Space models Hidden Markov model Kalman filter x 1! x 2! x n-1! x n! x n+1! o 1! o 2! o n-1! o n! o n+1!

5 Hidden Markov Models Note: a good background reference is LR Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proc. of the IEEE, Vol.77, No.2, pp , 1989.

6 Note: this picture is a state transition diagram, not a graphical model! Markov Chain Set of states, e.g. {S1,S2,S3,S4,S5} Table of transition probabilities (a ij = Prob of going from state S i to S j ) a 11 a 12 a 13 a 14 a 15 a 21 a 22 a 23 a 24 a 25 P(S a 31 a 32 a 33 a 34 a 35 j S i ) a 41 a 42 a 43 a 44 a 45 a 51 a 52 a 53 a 54 a 55 Prob of starting in each state e.g. π 1, π 2, π 3, π 4, π 5 P(S i ) Computation: prob of an ordered sequence S 2 S 1 S 5 S 4 P(S 2 ) P(S 1 S 2 ) P(S 5 S 1 ) P(S 4 S 5 ) = π 2 a 21 a 15 a 54

7 Hidden Markov Model {v1, v2, v3} {v1, v2, v3} {v1,v2,v3} {v1,v2,v3} {v1,v2,v3} Set of states, e.g. {S1,S2,S3,S4,S5} Table of transition probabilities (a ij = Prob of going from state S i to S j ) a 11 a 12 a 13 a 14 a 15 a 21 a 22 a 23 a 24 a 25 P(S a 31 a 32 a 33 a 34 a 35 j S i ) a 41 a 42 a 43 a 44 a 45 a 51 a 52 a 53 a 54 a 55 Prob of starting in each state e.g. π 1, π 2, π 3, π 4, π 5 P(S i ) Set of observable symbols, e.g. {v1,v2,v3} Prob of seeing each symbol in a given state (b ik = Prob seeing v k in state S i ) b 11 b 21 b 31 b 41 b 51 b 12 b 22 b 32 b 42 b 52 b 13 b 23 b 33 b 43 b 53 P(v k S i )

8 Hidden Markov Model Computation: Prob of ordered sequence (S 2,v 3 ) (S 1,v 1 ) (S 5,v 2 ) (S 4,v 1 ) P(S2,S1,S5,S4,v3,v1,v2,v1) = P(S 2 ) P(v 3 S 2 ) P(S 1 S 2 ) P(v 1 S 1 ) P(S 5 S 1 ) P(v 2 S 5 ) P(S 4 S 5 ) P(v 1 S 4 ) = π 2 b 23 a 21 b 11 a 15 b 52 a 54 b 41 = (π 2 a 21 a 15 a 54 ) ( b 23 b 11 b 52 b 41 ) = P(S1,S2,S3,S4) P(v1,v2,v3,v4 S1,S2,S3,S4)

9 HMM as Graphical Model Given HMM with states S1,S2,...,Sn, symbols v1,v2,...,vk, transition probs A={a ij }, initial probs Π={π i }, and observation probs B={b ik } Let state random variables be X1,X2,X3,X4,... with Xt being state at time t. Allowable values of Xt (a discrete random variable) are {S1,S2,...,Sn} Let observed random variables be O1,O2,O3... with Ot observed at time t. Allowable values of Ot (a discrete random variable) are {v1,v2,...,vk} Π A A A X1 X2 X3 X4 markov chain B B B B O1 O2 O3 O4 observations

10 HMM as Graphical Model Given HMM with states S1,S2,...,Sn, symbols v1,v2,...,vk, transition probs A={a ij }, initial probs Π={π i }, and observation probs B={b ik } Let state random variables be X1,X2,X3,X4,... with Xt being state at time t. Allowable values of Xt (a discrete random variable) are {S1,S2,...,Sn} Let observed random variables be O1,O2,O3... with Ot observed at time t. Allowable values of Ot (a discrete random variable) are {v1,v2,...,vk} Π A A A X1 X2 X3 X4 markov chain B B B B O1 O2 O3 O4 observations Oi are observed variables. Xj are hidden (latent) variables.

11 HMM as Graphical Model Verify: What is P(X1,X2,X3,X4,O1,O2,O3,O4)? Π A A A X1 X2 X3 X4 markov chain B B B B O1 O2 O3 O4 observations P(X1,X2,X3,X4,O1,O2,O3,O4) =... P(X1) P(O1 X1) P(X2 X1) P(O2 X2) P(X3 X2) P(O3 X3) P(X4 X3)P(O4 X4)

12 Three Computations for HMMs (From Rabiner tutorial)

13 HMM Problem 1 What is the likelihood of observing a sequence, e.g. O1,O2,O3? Note: there are multiple ways that a given sequence could be emitted, involving different sequences of hidden states X1,X2,X3 One (inefficient) way to compute the answer: generate all sequences of three states Sx,Sy,Sz and compute P(Sx,Sy,Sz,O1,O2,O3) [which we know how to do]. Summing up over all sequences of three states gives us our answer. Drawback, there are 3^N subsequences that can be formed from N states.

14 HMM Problem 1 Better (efficient) solution is based on message passing / belief propagation. marginal This is the sum-product procedure of message passing. It is called the forward-backward procedure in HMM terminology

15 HMM Problem 2 What is the most likely sequence of hidden states S1,S2,S3 given that we have observed O1,O2,O3? X X Again, there is an inefficient way based on explicitly generating the exponential number of possible state sequences... AND, there is a more efficient way using message passing.

16 HMM Problem 2 X X X Define: max Φ(X)= max max Then X Φ(X) This is the max-product procedure of message passing. It is called the Viterbi algorithm in HMM terminology

17 Viterbi, under the hood 1) Build a state-space trellis. 2) Add a source and sink node. 4) Assign weights to edges based on observations. 3) Given observations. X1 S1 X2 X3 X4 O1 O2 O3 O4 S1 S1 S1 B S2 S3 S2 S3 S2 S3 S2 S3 E P(S i ) S4 S4 S4 P(O1 S i )P(S j S i ) P(O2 S i )P(S j S i ) S4 P(O3 S i )P(S j S i ) P(O4 S i ) Do multistage dynamic programming to find the max product path. (note: in some implementations, each edge is weighted by -log(w i ) of the weights shown here, so that standard min length path DP can be performed.)

18 HMM Problem 3 Given a training set of observation sequences {{O1,O2,...}, {O1,O2,...}, {O1,O2,...}} how do we determine the transition probs a ij, initial state probs p i, and observation probs b ik? This is a learning problem. It is the hardest problem of the three. We assume topology of the HMM is known (number and connectivity of the states) otherwise it is even harder. Two popular approaches: Segmental K-means algorithm Baum-Welch algorithm (EM-based) Recall clustering lectures earlier in the semester!

19 Steve Mills, U.Nottingham

20 Steve Mills, U.Nottingham

21 Segmental K-Means Steve Mills, U.Nottingham

22 Note: this is essentially k-means clustering! Steve Mills, U.Nottingham

23 Steve Mills, U.Nottingham

24 Steve Mills, U.Nottingham

25 Steve Mills, U.Nottingham

26 Steve Mills, U.Nottingham

27 Steve Mills, U.Nottingham

28 Steve Mills, U.Nottingham

29 Steve Mills, U.Nottingham

30 Note: This part assumes continuous-valued observations are output. For a discrete set of observation symbols, we could just do histograms here. Steve Mills, U.Nottingham

31 Steve Mills, U.Nottingham

32 Note: For a discrete set of observation symbols, we could use histograms normalized to sum to 1 as estimates of the probability mass function of outputting each symbol. Steve Mills, U.Nottingham

33 Steve Mills, U.Nottingham

34 Steve Mills, U.Nottingham

35 Steve Mills, U.Nottingham

36 Really? Steve Mills, U.Nottingham

37 Baum-Welch Algorithm Same thing, but with fractional assignments of observations to states. Basic idea is to use EM instead of K-means as a way of assessing ownership weights before computing observation statistics at each state.

38 Kalman Filter Note: a good background reference is An Introduction to the Kalman Filter, Greg Welch and Gary Bishop, University of North Carolina at Chapel Hill, Dept of Computer Science Tech Report Welch also maintains an excellent website about all things Kalman:

39 Kalman Filter for Tracking P(x1,x2,x3,x4,...,y1,y2,y3,y4,...) = P(x1)P(y1 x1)p(x2 x1)p(y2 x2)p(x3 x2)p(y3 x3)p(x4 x3)p(y4 x4)... x 1! x 2! x n-1! x n! x n+1! y 1! y 2! y n-1! y n! y n+1!

40 Kalman Filter for Tracking P(x1,x2,x3,x4,...,y1,y2,y3,y4,...) = P(x1)P(y1 x1)p(x2 x1)p(y2 x2)p(x3 x2)p(y3 x3)p(x4 x3)p(y4 x4)... x 1! x 2! x n-1! x n! x n+1! y 1! y 2! y n-1! y n! y n+1! What we typically want to compute for tracking applications is P(xn y1, y2,..., yn)

41 Linear Dynamical Systems Next state is a linear function of current state + zero-mean Gaussian noise Each observation is a linear function of current state + zeromean Gaussian noise Initial prior distribution on first state is Gaussian => All distributions remain Gaussian! => Means and covariances can be estimated over time by a Kalman filter

42 Assume: Kalman Filter Derivation (in 1D) Linear motion and measurement models: Implied by motion and measurement models:

43 Kalman Filter Derivation x k-1! x k! y k-1! y k! What is P(x k y 1,...,y k )?

44 Derivation Strategy Combine P(x k-1 y 1,...,y k-1 ) and P(x k x k-1 ) to compute P(x k y 1,...,y k-1 ) x k-1! x k! y k-1! y k! Step 1: motion prediction

45 Derivation Strategy Combine P(x k y 1,...,y k-1 ) and P(y k x k ) to compute P(x k y 1,...,y k ) x k-1! x k! y k-1! y k! Step 2: data correction

46 Robert Collins Step 1: Motion Prediction Combine P(xk-1 y1,...,yk-1) and P(xk xk-1) to compute P(xk y1,...,yk-1) Quadratic form in xk-1 and xk. Therefore this is a joint Gaussian distribution over xk-1 and xk. Integrated over xk-1 yields marginal distribution P(xk y1,...,yk-1)

47 Robert Collins Step 2: Data Correction Combine P(xk y1,...,yk-1) and P(yk xk) to compute P(xk y1,...,yk) Through some algebra (completing the square), we find that:

48 Robert Collins Step 2: Data Correction Combine P(xk y1,...,yk-1) and P(yk xk) to compute P(xk y1,...,yk) Through some algebra (completing the square), we find that: This becomes the Gaussian prior for the next iteration of motion prediction and data correction. Thus we continue propagating forward into subsequent time steps.

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