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Graphical Models for chains, trees and grids
1.
Graphical Models for
Chains, Trees, and Grids Gabriel Brostow UCL
2.
Sources • Book
and slides by Simon Prince: “Computer vision: models, learning and inference” (June 2012) • See more on www.computervisionmodels.com 2
3.
Part 1: Graphical
Models for Chains and Trees 3
4.
Part 1 Structure
• Chain and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 4 4 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Extra Extra
5.
Example Problem: Pictorial
Structures 5 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
6.
Chain and tree
models • Given a set of measurements and world states , infer the world states from the measurements. • Problem: if N is large, then the model relaOng the two will have a very large number of parameters. • SoluOon: build sparse models where we only describe subsets of the relaOons between variables. 6 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
7.
Chain and tree
models 7 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Chain model: only model connecOons between a world variable and its 1 preceeding and 1 subsequent variables Tree model: connecOons between world variables are organized as a tree (no loops). Disregard direcOonality of connecOons for directed model
8.
AssumpOons 8 Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince We’ll assume that – World states are discrete – Observed data variables for each world state – The nth data variable is condi&onally independent of all other data variables and world states, given associated world state
9.
See also: Thad
Starner’s work Gesture Tracking 9 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
10.
Directed model for
chains (Hidden Markov model) 10 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince CompaObility of measurement and world state CompaObility of world state and previous world state
11.
Undirected model for
chains 11 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince CompaObility of measurement and world state CompaObility of world state and previous world state
12.
Equivalence of chain
models 12 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Directed: Undirected: Equivalence:
13.
Chain model for
sign language applicaOon 13 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince ObservaOons are normally distributed but depend on sign k World state is categorically distributed, parameters depend on previous world state
14.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 14 14 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
15.
MAP inference in
chain model 15 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince MAP inference: SubsOtuOng in : Directed model:
16.
MAP inference in
chain model 16 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Takes the general form: Unary term: Pairwise term:
17.
Dynamic programming 17
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Maximizes funcOons of the form: Set up as cost for traversing graph – each path from le` to right is one possible configuraOon of world states
18.
Dynamic programming 18
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Algorithm: 1. Work through graph compuOng minimum possible cost to reach each node 2. When we get to last column, find minimum 3. Trace back to see how we got there
19.
Worked example 19
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Unary cost Pairwise costs: • Zero cost to stay at same label • Cost of 2 to change label by 1 • Infinite cost for changing by more than one (not shown)
20.
Worked example 20
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Minimum cost to reach first node is just unary cost
21.
Worked example 21
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Minimum cost is minimum of two possible routes to get here Route 1: 2.0+0.0+1.1 = 3.1 Route 2: 0.8+2.0+1.1 = 3.9
22.
Worked example 22
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Minimum cost is minimum of two possible routes to get here Route 1: 2.0+0.0+1.1 = 3.1 -‐-‐ this is the minimum – note this down Route 2: 0.8+2.0+1.1 = 3.9
23.
Worked example 23
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince General rule:
24.
Worked example 24
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Work through the graph, compuOng the minimum cost to reach each node
25.
Worked example 25
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Keep going unOl we reach the end of the graph
26.
Worked example 26
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Find the minimum possible cost to reach the final column
27.
Worked example 27
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Trace back the route that we arrived here by – this is the minimum configuraOon
28.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 28 28 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
29.
MAP inference for
trees 29 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
30.
MAP inference for
trees 30 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
31.
Worked example 31
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
32.
Worked example 32
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Variables 1-‐4 proceed as for the chain example.
33.
Worked example 33
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince At variable n=5 must consider all pairs of paths from into the current node.
34.
Worked example 34
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Variable 6 proceeds as normal. Then we trace back through the variables, splilng at the juncOon.
35.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 35 35 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Extra Extra Jump there
36.
Marginal posterior inference
36 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Start by compuOng the marginal distribuOon over the Nth variable • Then we`ll consider how to compute the other marginal distribuOons
37.
CompuOng one marginal
distribuOon 37 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Compute the posterior using Bayes` rule: We compute this expression by wriOng the joint probability :
38.
CompuOng one marginal
distribuOon 38 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Problem: CompuOng all NK states and marginalizing explicitly is intractable. SoluOon: Re-‐order terms and move summaOons to the right
39.
CompuOng one marginal
distribuOon 39 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Define funcOon of variable w1 (two rightmost terms) Then compute funcOon of variables w2 in terms of previous funcOon Leads to the recursive relaOon
40.
CompuOng one marginal
distribuOon 40 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince We work our way through the sequence using this recursion. At the end we normalize the result to compute the posterior Total number of summaOons is (N-‐1)K as opposed to KN for brute force approach.
41.
Forward-‐backward algorithm 41
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • We could compute the other N-‐1 marginal posterior distribuOons using a similar set of computaOons • However, this is inefficient, as much of the computaOon is duplicated • The forward-‐backward algorithm computes all of the marginal posteriors at once SoluOon: Compute all first term using a recursion Compute all second terms using a recursion ... and take products
42.
Forward recursion 42
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Using condiOonal independence relaOons CondiOonal probability rule This is the same recursion as before
43.
Backward recursion 43
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Using condiOonal independence relaOons CondiOonal probability rule This is another recursion of the form
44.
Forward backward algorithm
44 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Compute the marginal posterior distribuOon as product of two terms Forward terms: Backward terms:
45.
Belief propagaOon 45
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Forward backward algorithm is a special case of a more general technique called belief propagaOon • Intermediate funcOons in forward and backward recursions are considered as messages conveying beliefs about the variables. • We’ll examine the Sum-‐Product algorithm. • The sum-‐product algorithm operates on factor graphs.
46.
Sum product algorithm
46 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Forward backward algorithm is a special case of a more general technique called belief propagaOon • Intermediate funcOons in forward and backward recursions are considered as messages conveying beliefs about the variables. • We’ll examine the Sum-‐Product algorithm. • The sum-‐product algorithm operates on factor graphs.
47.
Factor graphs 47
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • One node for each variable • One node for each funcOon relaOng variables
48.
Sum product algorithm
48 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Forward pass • Distribute evidence through the graph Backward pass • Collates the evidence Both phases involve passing messages between nodes: • The forward phase can proceed in any order as long as the outgoing messages are not sent unOl all incoming ones received • Backward phase proceeds in reverse order to forward
49.
Sum product algorithm
49 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Three kinds of message • Messages from unobserved variables to funcOons • Messages from observed variables to funcOons • Messages from funcOons to variables
50.
Sum product algorithm
50 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message type 1: • Messages from unobserved variables z to funcOon g • Take product of incoming messages • InterpretaOon: combining beliefs Message type 2: • Messages from observed variables z to funcOon g • InterpretaOon: conveys certain belief that observed values are true
51.
Sum product algorithm
51 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message type 3: • Messages from a funcOon g to variable z • Takes beliefs from all incoming variables except recipient and uses funcOon g to a belief about recipient CompuOng marginal distribuOons: • A`er forward and backward passes, we compute the marginal dists as the product of all incoming messages
52.
Sum product: forward
pass 52 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from x1 to g1: By rule 2:
53.
Sum product: forward
pass 53 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from g1 to w1: By rule 3:
54.
Sum product: forward
pass 54 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from w1 to g1,2: By rule 1: (product of all incoming messages)
55.
Sum product: forward
pass 55 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from g1,2 from w2: By rule 3:
56.
Sum product: forward
pass 56 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Messages from x2 to g2 and g2 to w2:
57.
Sum product: forward
pass 57 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from w2 to g2,3: The same recursion as in the forward backward algorithm
58.
Sum product: forward
pass 58 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from w2 to g2,3:
59.
Sum product: backward
pass 59 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from wN to gN,N-1:
60.
Sum product: backward
pass 60 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from gN,N-1 to wN-1:
61.
Sum product: backward
pass 61 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Message from gn,n-1 to wn-1: The same recursion as in the forward backward algorithm
62.
Sum product: collaOng
evidence 62 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Marginal distribuOon is products of all messages at node • Proof:
63.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 63 63 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
64.
Marginal posterior inference
for trees 64 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Apply sum-‐product algorithm to the tree-‐structured graph.
65.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 65 65 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
66.
Tree structured graphs
66 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince This graph contains loops But the associated factor graph has structure of a tree Can sOll use Belief PropagaOon
67.
Learning in chains
and trees 67 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Supervised learning (where we know world states wn) is relaOvely easy. Unsupervised learning (where we do not know world states wn) is more challenging. Use the EM algorithm: • E-‐step – compute posterior marginals over states • M-‐step – update model parameters For the chain model (hidden Markov model) this is known as the Baum-‐Welch algorithm.
68.
Grid-‐based graphs 68
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince O`en in vision, we have one observaOon associated with each pixel in the image grid.
69.
Why not dynamic
programming? 69 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince When we trace back from the final node, the paths are not guaranteed to converge.
70.
Why not dynamic
programming? 70 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
71.
Why not dynamic
programming? 71 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince But:
72.
Approaches to inference
for grid-‐based models 72 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 1. Prune the graph. Remove edges unOl an edge remains
73.
73 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince 2. Combine variables. Merge variables to form compound variable with more states unOl what remains is a tree. Not pracOcal for large grids Approaches to inference for grid-‐based models
74.
74 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Approaches to inference for grid-‐based models 3. Loopy belief propagaOon. Just apply belief propagaOon. It is not guaranteed to converge, but in pracOce it works well. 4. Sampling approaches Draw samples from the posterior (easier for directed models) 5. Other approaches • Tree-‐reweighted message passing • Graph cuts
75.
Structure • Chain
and tree models • MAP inference in chain models • MAP inference in tree models • Maximum marginals in chain models • Maximum marginals in tree models • Models with loops • ApplicaOons 75 75 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
76.
76 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Gesture Tracking
77.
Stereo vision 77
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Two image taken from slightly different posiOons • Matching point in image 2 is on same scanline as image 1 • Horizontal offset is called disparity • Disparity is inversely related to depth • Goal – infer dispariOes wm,n at pixel m,n from images x(1) and x(2) Use likelihood:
78.
Stereo vision 78
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
79.
Stereo vision 79
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 1. Independent pixels
80.
Stereo vision 80
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 2. Scanlines as chain model (hidden Markov model)
81.
Stereo vision 81
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 3. Pixels organized as tree (from Veksler 2005)
82.
Pictorial Structures 82
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
83.
SegmentaOon 83 Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince
84.
Part 1 Conclusion
84 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • For the special case of chains and trees we can perform MAP inference and compute marginal posteriors efficiently. • Unfortunately, many vision problems are defined on pixel grids – this requires special methods
85.
Part 2: Graphical
Models for Grids 85
86.
86 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Stereo vision Example ApplicaOon
87.
Part 2 Structure
• Denoising problem • Markov random fields (MRFs) • Max-‐flow / min-‐cut • Binary MRFs -‐ submodular (exact soluOon) • MulO-‐label MRFs – submodular (exact soluOon) • MulO-‐label MRFs -‐ non-‐submodular (approximate) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 87
88.
Models for grids
88 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince • Consider models with one unknown world state at each pixel in the image – takes the form of a grid. • Loops in the graphical model, so cannot use dynamic programming or belief propagaOon • Define probability distribuOons that favor certain configuraOons of world states – Called Markov random fields – Inference using a set of techniques called graph cuts
89.
89 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Binary Denoising Before A`er Image represented as binary discrete variables. Some proporOon of pixels randomly changed polarity.
90.
90 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince MulO-‐label Denoising Before A`er Image represented as discrete variables represenOng intensity. Some proporOon of pixels randomly changed according to a uniform distribuOon.
91.
91 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Denoising Goal Observed Data Uncorrupted Image
92.
• Most of
the pixels stay the same • Observed image is not as smooth as original Now consider pdf over binary images that encourages smoothness – Markov random field 92 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Denoising Goal Observed Data Uncorrupted Image
93.
93 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Markov random fields This is just the typical property of an undirected model. We’ll conOnue the discussion in terms of undirected models
94.
94 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Markov random fields Normalizing constant (parOOon funcOon) PotenOal funcOon Returns posiOve number Subset of variables (clique)
95.
95 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Markov random fields Normalizing constant (parOOon funcOon) Cost funcOon Returns any number Subset of variables (clique) RelaOonship
96.
96 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Smoothing Example
97.
97 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Smoothing Example Smooth soluOons (e.g. 0000,1111) have high probability Z was computed by summing the 16 un-‐normalized probabiliOes
98.
98 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Smoothing Example Samples from larger grid -‐-‐ mostly smooth Cannot compute parOOon funcOon Z here -‐ intractable
99.
99 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Denoising Goal Observed Data Uncorrupted Image
100.
100 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Denoising overview Bayes’ rule: Likelihoods: Prior: Markov random field (smoothness) MAP Inference: Graph cuts Probability of flipping polarity
101.
101 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Denoising with MRFs Observed image, x Original image, w MRF Prior (pairwise cliques) Inference : Likelihoods
102.
MAP Inference 102
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince Unary terms (compatability of data with label y) Pairwise terms (compatability of neighboring labels)
103.
103 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Graph Cuts Overview Unary terms (compatability of data with label y) Pairwise terms (compatability of neighboring labels) Graph cuts used to opOmise this cost funcOon: Three main cases:
104.
104 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Graph Cuts Overview Unary terms (compatability of data with label y) Pairwise terms (compatability of neighboring labels) Graph cuts used to opOmise this cost funcOon: Approach: Convert minimizaOon into the form of a standard CS problem, MAXIMUM FLOW or MINIMUM CUT ON A GRAPH Polynomial-‐Ome methods for solving this problem are known
105.
105 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Max-‐Flow Problem Goal: To push as much ‘flow’ as possible through the directed graph from the source to the sink. Cannot exceed the (non-‐negaOve) capaciOes cij associated with each edge.
106.
106 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Saturated Edges When we are pushing the maximum amount of flow: • There must be at least one saturated edge on any path from source to sink (otherwise we could push more flow) • The set of saturated edges hence separate the source and sink
107.
107 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths Two numbers represent: current flow / total capacity
108.
Choose any route
from source to sink with spare capacity, and push as much flow as you can. One edge (here 6-‐t) will saturate. 108 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths
109.
109 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths Choose another route, respecOng remaining capacity. This Ome edge 6-‐5 saturates.
110.
110 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths A third route. Edge 1-‐4 saturates
111.
111 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths A fourth route. Edge 2-‐5 saturates
112.
112 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths A fi`h route. Edge 2-‐4 saturates
113.
There is now
no further route from source to sink – there is a saturated edge along every possible route (highlighted arrows) 113 Computer vision: models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths
114.
114 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince AugmenOng Paths The saturated edges separate the source from the sink and form the min-‐cut soluOon. Nodes either connect to the source or connect to the sink.
115.
Graph Cuts:
Binary MRF Unary terms (compatability of data with label w) Pairwise terms (compatability of neighboring labels) Graph cuts used to opOmise this cost funcOon: First work with binary case (i.e. True label w is 0 or 1) Constrain pairwise costs so that they are “zero-‐diagonal” Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 115
116.
Graph ConstrucOon •
One node per pixel (here a 3x3 image) • Edge from source to every pixel node • Edge from every pixel node to sink • Reciprocal edges between neighbours Note that in the minimum cut EITHER the edge connecOng to the source will be cut, OR the edge connecOng to the sink, but NOT BOTH (unnecessary). Which determines whether we give that pixel label 1 or label 0. Now a 1 to 1 mapping between possible labelling and possible minimum cuts Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 116
117.
Graph ConstrucOon Now
add capaciOes so that minimum cut, minimizes our cost funcOon Unary costs U(0), U(1) avached to links to source and sink. • Either one or the other is paid. Pairwise costs between pixel nodes as shown. • Why? Easiest to understand with some worked examples. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 117
118.
Example 1 Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince 118
119.
Example 2 Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince 119
120.
Example 3 Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince 120
121.
Computer vision: models,
learning and inference. ©2011 Simon J.D. Prince 121
122.
Graph Cuts:
Binary MRF Unary terms (compatability of data with label w) Pairwise terms (compatability of neighboring labels) Graph cuts used to opOmise this cost funcOon: Summary of approach • Associate each possible soluOon with a minimum cut on a graph • Set capaciOes on graph, so cost of cut matches the cost funcOon • Use augmenOng paths to find minimum cut • This minimizes the cost funcOon and finds the MAP soluOon Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 122
123.
General Pairwise costs
Modify graph to • Add P(0,0) to edge s-‐b • Implies that soluOons 0,0 and 1,0 also pay this cost • Subtract P(0,0) from edge b-‐a • SoluOon 1,0 has this cost removed again Similar approach for P(1,1) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 123
124.
ReparameterizaOon The max-‐flow
/ min-‐cut algorithms require that all of the capaciOes are non-‐negaOve. However, because we have a subtracOon on edge a-‐b we cannot guarantee that this will be the case, even if all the original unary and pairwise costs were posiOve. The soluOon to this problem is reparamaterizaOon: find new graph where costs (capaciOes) are different but choice of minimum soluOon is the same (usually just by adding a constant to each soluOon) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 124
125.
ReparameterizaOon 1 The
minimum cut chooses the same links in these two graphs Computer vision: models, learning and inference. ©2011 Simon J.D. Prince
126.
ReparameterizaOon 2 The
minimum cut chooses the same links in these two graphs Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 126
127.
Submodularity Adding together
implies Subtract constant β Add constant, β Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 127
128.
Submodularity If this
condiOon is obeyed, it is said that the problem is “submodular” and it can be solved in polynomial Ome. If it is not obeyed then the problem is NP hard. Usually it is not a problem as we tend to favour smooth soluOons. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 128
129.
Denoising Results Original
Pairwise costs increasing Pairwise costs increasing Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 129
130.
Plan of Talk
• Denoising problem • Markov random fields (MRFs) • Max-‐flow / min-‐cut • Binary MRFs – submodular (exact soluOon) • MulO-‐label MRFs – submodular (exact soluOon) • MulO-‐label MRFs -‐ non-‐submodular (approximate) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 130
131.
ConstrucOon for two
pixels (a and b) and four labels (1,2,3,4) There are 5 nodes for each pixel and 4 edges between them have unary costs for the 4 labels. One of these edges must be cut in the min-‐cut soluOon and the choice will determine which label we assign. MulOple Labels Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 131
132.
Constraint Edges The
edges with infinite capacity poinOng upwards are called constraint edges. They prevent soluOons that cut the chain of edges associated with a pixel more than once (and hence given an ambiguous labelling) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 132
133.
MulOple Labels Inter-‐pixel
edges have costs defined as: Superfluous terms : For all i,j where K is number of labels Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 133
134.
Example Cuts Must
cut links from before cut on pixel a to a`er cut on pixel b. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 134
135.
Pairwise Costs If
pixel a takes label I and pixel b takes label J Must cut links from before cut on pixel a to a`er cut on pixel b. Costs were carefully chosen so that sum of these links gives appropriate pairwise term. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 135
136.
ReparameterizaOon Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince 136
137.
Submodularity We require
the remaining inter-‐pixel links to be posiOve so that or By mathemaOcal inducOon we can get the more general result Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 137
138.
Submodularity If not
submodular, then the problem is NP hard. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 138
139.
Convex vs. non-‐convex
costs QuadraOc • Convex • Submodular Truncated QuadraOc • Not Convex • Not Submodular Povs Model • Not Convex • Not Submodular Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 139
140.
What is wrong
with convex costs? • Pay lower price for many small changes than one large one • Result: blurring at large changes in intensity Observed noisy image Denoised result Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 140
141.
Plan of Talk
• Denoising problem • Markov random fields (MRFs) • Max-‐flow / min-‐cut • Binary MRFs -‐ submodular (exact soluOon) • MulO-‐label MRFs – submodular (exact soluOon) • MulO-‐label MRFs -‐ non-‐submodular (approximate) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 141
142.
Alpha Expansion Algorithm
• break mulOlabel problem into a series of binary problems • at each iteraOon, pick label α and expand (retain original or change to α) IniOal labelling IteraOon 1 (orange) IteraOon 3 (red) IteraOon 2 (yellow) Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 142
143.
Alpha Expansion Ideas
• For every iteraOon – For every label – Expand label using opOmal graph cut soluOon Co-‐ordinate descent in label space. Each step opOmal, but overall global maximum not guaranteed Proved to be within a factor of 2 of global opOmum. Requires that pairwise costs form a metric: Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 143
144.
Alpha Expansion ConstrucOon
Binary graph cut – either cut link to source (assigned to α) or to sink (retain current label) Unary costs avached to links between source, sink and pixel nodes appropriately. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 144
145.
Alpha Expansion ConstrucOon
Graph is dynamic. Structure of inter-‐pixel links depends on α and the choice of labels. There are four cases. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 145
146.
Alpha Expansion ConstrucOon
Case 1: Adjacent pixels both have label α already. Pairwise cost is zero – no need for extra edges. Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 146
147.
Alpha Expansion ConstrucOon
Case 2: Adjacent pixels are α,β. Result either • α,α (no cost and no new edge). • α,β (P(α,β), add new edge). Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 147
148.
Alpha Expansion ConstrucOon
Case 3: Adjacent pixels are β,β. Result either • β,β (no cost and no new edge). • α,β (P(α,β), add new edge). • β,α (P(β,α), add new edge). Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 148
149.
Alpha Expansion ConstrucOon
Case 4: Adjacent pixels are β,γ. Result either • β,γ (P(β,γ), add new edge). • α,γ (P(α,γ), add new edge). • β,α (P(β,α), add new edge). • α,α (no cost and no new edge). Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 149
150.
Example Cut 1
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 150
151.
Example Cut 1
Important! Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 151
152.
Example Cut 2
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 152
153.
Example Cut 3
Computer vision: models, learning and inference. ©2011 Simon J.D. Prince 153
154.
Denoising Results Computer
vision: models, learning and inference. ©2011 Simon J.D. Prince 154
155.
155 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince CondiOonal Random Fields
156.
156 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Directed model for grids Cannot use graph cuts as three-‐wise term. Easy to draw samples.
157.
157 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Background subtracOon ApplicaOons
158.
158 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Grab cut ApplicaOons
159.
159 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Shi`-‐map image ediOng ApplicaOons
160.
160 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince
161.
161 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Shi`-‐map image ediOng ApplicaOons
162.
162 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Super-‐resoluOon ApplicaOons
163.
163 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Texture synthesis ApplicaOons
164.
164 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince Image QuilOng
165.
165 Computer vision:
models, learning and inference. ©2011 Simon J.D. Prince • Synthesizing faces ApplicaOons
166.
Further resources •
hvp://www.computervisionmodels.com/ – Code – Links + readings (for these and other topics) • Conference papers online: BMVC, CVPR, ECCV, ICCV, etc. • Jobs mailing lists: Imageworld, Visionlist 166
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