MVA ENS Cachan. Lecture 2: Logistic regression & intro to MIL Iasonas Kokkinos


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1 Machine Learning for Computer Vision 1 MVA ENS Cachan Lecture 2: Logistic regression & intro to MIL Iasonas Kokkinos Department of Applied Mathematics Ecole Centrale Paris Galen Group INRIASaclay
2 Administrative details 2 Make sure you have all received my from last week
3 Lecture outline 3 Recap & problems of linear regression Logistic Regression Introduction to Multiple Instance Learning
4 4 Learning problem Recover inputoutput mapping Output: y Input: x Method: f Parameters: w Aspects of the learning problem Identify methods that fit the problem setting Determine parameters that properly classify the training set Measure and control the complexity of these functions
5 Linear Classifiers Find linear expression (hyperplane) to separate positive and negative examples 5 Feature coordinate j x x i i positive : negative : x x i i w + b w + b 0 < 0 Each data point has a class label: +1 ( ) y t = 1 ( ) Feature coordinate i
6 Learning problem formulation 6 Given: Training set of featurelabel pairs Wanted: simple that works well for simple: penalizing function complexity, to guarantee generalization works well: quantified by loss criterion
7 Linear regression 7 Linear Loss function: quantify appropriateness of for Introduce vector notation
8 Inappropriateness of quadratic loss We chose the quadratic cost function for convenience Single, global minimum & closed form expression 8 But does it indicate classification performance? Computed Decision Boundary Linear Fit Desired decision boundary
9 Inappropriateness of quadratic loss Consider transformation: 9 Quadratic loss is not robust to outliers and penalizes outputs that are `too good
10 Lecture outline 10 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Multiple Instance Learning
11 Probabilistic interpretation of least squares 11 Outputs: continuous random variables y = hx, wi +, N(0, ) linear function of inputs + zero mean Gaussian r.v. Optimal w: maximizes likelihood of observed outputs But the labels are discrete!
12 A probabilistic criterion for training a classifier 12 Training set: y: discrete observations: model as samples from Bernoulli distribution Find w that maximizes the likelihood of labels in the training set
13 Sigmoidal function & logistic regression 13 sigmoidal Training criterion from previous slide: How do we optimize it with respect to w? What does it mean?
14 Lecture outline 14 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Multiple Instance Learning
15 Maximization by gradient ascent 15 Gradient ascent:
16 Maximization with NewtonRaphson 16 Newton method for finding roots of 1D function : Newton method for finding maxima of 1D function : condition NewtonRaphson method for finding maxima of ND function :
17 NewtonRaphson for Logistic Regression 17 Gradient: Hessian: In statistics: Iteratively Reweighted Least Squares (IRLS)
18 Lecture outline 18 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Support Vector Machines
19 A compact expression for the loss 19 Using, :
20 Log loss: 20
21 Log loss vs. quadratic loss 21
22 Logistic vs Linear Regression 22 Logistic regression is more robust Linear Regression Logistic Regression
23 Lecture outline 23 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Multiple Instance Learning
24 Boundary detection problem Object/Surface Boundaries 24
25 25 Signallevel challenges Poor contrast Shadows Texture
26 Machine Learning for Computer Vision Lecture 2 Fundamental challenges: can humans do it? 26
27 Learningbased approaches 27 Boundary or nonboundary? Use humanannotated segmentations Use any visual cue as input to the decision function. Use decision trees/logisitic regression/boosting/ and learn to combine the individual inputs. S. Konishi, A.Yuille, J. Coughlan, S.C. Zhu, Statistical Edge Detection: Learning and Evaluating Edge Cues, IEEE PAMI, 2003 D. Martin, C. Fowlkes, J. Malik. "Learning to Detect Natural Image Boundaries Using Local Brightness, Color and Texture Cues", IEEE PAMI, 2004
28 Evaluation protocol 28 Threshold detector s output at certain level Match outputs to human groundtruth Count true/false positives (t/f)positives, misses (m) From precision, p and recall, r Quantify performance in terms of Fmeasure
29 29 Contours can be defined by any of a number of cues (P. Cavanagh) Slide credit: J. Malik, M. Maire
30 Cuelocalization Gray level photographs 30 Objects from motion Objects from luminance Objects from disparity Line drawings Objects from texture Slide credit: J. Malik, M. Maire GrillSpector et al., Neuron 1998
31 31 Local boundary cues Separate features per candidate orientation In specific: r (x,y) θ 1976 CIE L*a*b* colorspace Brightness Gradient BG(x,y,r,θ) Difference of L* distributions Color Gradient CG(x,y,r,θ) Difference of a*b* distributions Texture Gradient TG(x,y,r,θ) Difference of distributions of V1like filter responses Slide credit: J. Malik, M. Maire
32 Boundary cues (horizontal) 32 Brightness Color (a, b channels) Texture
33 Boundary π/4 33 Brightness Color (a, b channels) Texture
34 Brightness π/2 34 Brightness Color (a, b channels) Texture
35 Brightness 3π/4 35 Brightness Color (a, b channels) Texture
36 Cue combinations with logistic regression 36 Slide credit: J. Malik, M. Maire
37 Exploiting global constraints: image Segmentation as Graph Partitioning Build a weighted graph G=(V,E) from image V: image pixels 37 E: connections between pairs of nearby pixels Partition graph so that similarity within group is large and similarity between groups is small  Normalized Cuts [Shi & Malik 97] Slide credit: J. Malik, M. Maire
38 38 Wij small when intervening contour strong, strong when weak.. Slide credit: J. Malik, M. Maire
39 Normalized Cuts as a SpringMass system 39 Each pixel is a point mass; each connection is a spring: ( D W ) y = λdy Fundamental modes are generalized eigenvectors of (D  W) x = λdx Slide credit: J. Malik, M. Maire
40 Contour information from eigenvectors 40 Slide credit: J. Malik, M. Maire
41 Classifier 41 Logistic regression
42 Progress during the last 40 years 42 Humans Berkeley gpb, 08 Berkeley PB, 04 Canny+ Hysteresis ( 85) Prewitt, good feature= 5 years of work
43 Lecture outline 43 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Multiple Instance Learning
44 Learning boundary detection 44 Problem I: inconsistent orientation information Problem II: inconsistent location information
45 Sneaking into the fun room 45 mom s keychain grandma s keychain dad s keychain We know that dad cannot enter the fun room, either Which key should we try? Slide Credit: B. Babenko/T. Dietterich
46 Multiple Instance Learning 46 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
47 Noisyor classifier combination N classifier responses, {p 1,...,p N } p i : probability of positive label (classifier i) 1 p i : probability of negative (classifier i) Negative bag: all instance classifiers are negative NY (1 p i ) p =1 i=1 Probability of bag being positive: NY (1 p i ) i=1 47
48 Progress during the last decade Precision : Humans 0.750: Fused (ours + others) 0.741: MIL detector (ours) 0.739: Sparse Code Gradients 0.728: Sketch Tokens 0.714: GlobalPb Recall PrecisionRecall Curves on the Berkeley Benchmark
49 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold gpb 49
50 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold Our results 50
51 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold gpb 51
52 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold Our results 52
53 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold gpb 53
54 Machine Learning for Computer Vision Lecture 2 Comparisons with optimal threshold Our results 54
55 Machine Learning for Computer Vision Lecture 2 Learning symmetry detection S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV
56 Multiscale and multiorientation features 56 S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV 2012
57 Algorithm pipeline 57 Input image P(x,y) Features Brightness Texture Color Spectra l P(x,y) Training Multiple Instance Learning (MIL) nonmaximum suppression softmax θ,s (P(x,y,θ,s)) 14x1 weight vector S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV 2012
58 58 Feature extraction Color: CIE Lab color space. Texture: texton map. Hard binning (32 bins for 3 color channels, 64 textons). Rectangle filters extract features at multiple scales and orientations. Differences of histograms ( gradients ) of color and texture content à symmetry indication. χ 2 distance à dissimilarity of feature content between adjacent rectangles. Integral images for fast extraction. S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV 2012
59 MIL Training 59 Input image θ = 22.5 θ = 45 Varying angle θ = 90 θ = 135 Varying scale θ = MIL training s = 4 s = 8 w w w =! w s = 12 s = 24 s = 16 Probability map S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV 2012
60 60 Machine Learning for Computer Vision Lecture 2 Results Lindeberg Levinshtein This work Stateoftheart performance Large boost from color, texture and spectral cues. Long contours (region proposals) S. Tsogkas & I.K., Learningbased symmetry detection in natural images, ECCV 2012 Groundtruth
61 61 Machine Learning for Computer Vision Lecture 2 Some more results Lindeberg Levinshtein This work Groundtruth
62 Lecture recap 62 Logistic Regression Training criterion Optimization Application to boundary detection Introduction to MIL
63 Logistic regression training cost 63 Log loss Quadratic loss
64 Lecture recap 64 Logistic Regression Training criterion Optimization Application to boundary detection Introduction to MIL
65 Maximization with NewtonRaphson 65 NewtonRaphson method for finding maxima of ND function: Condition for maximum: Jacobian: Hessian:
66 Lecture recap 66 Logistic Regression Training criterion Optimization Application to boundary detection Introduction to MIL
67 Progress during the last 40 years 67 Humans Berkeley gpb, 08 Berkeley PB, 04 Canny+ Hysteresis ( 85) Prewitt, good feature= 5 years of work
68 Lecture outline 68 Recap & problems of linear regression Logistic Regression Training criterion formulation Optimization Interpretation Application to boundary detection Introduction to Multiple Instance Learning
69 Appendix 69 Newton Raphson = Iteratively Reweighted Least Squares Masking problem in multiclass linear regression & softmax
70 Newton Raphson = Iteratively Reweighted Least Squares 70 Newton Raphson: Hessian: Rewrite Update: Transform: Weighted Least Squares Fit:
71 Multiple classes & linear regression 71 K classes: oneofk coding 4 classes, ith sample is in 3 rd class: Matrix notation: Loss function: Least squares fit:
72 Multiple classes & linear regression 72 One linear discriminant per class: Problem: ambiguous regions Solution: assign to discriminant with largest score
73 Masking Problem in linear regression Class 1 Class 2 Class 3 73 Nothing ever gets assigned to class 2! 2D version:
74 Multiple classes & logistic regression 74 Soft maximum of competing classes: Discriminants (inputs) Softmax (outputs) Label probability: Similar steps for parameter estimation (Bishop s book)
75 Logistic vs Linear Regression, n>2 classes 75 Linear regression Logistic regression Logistic regression does not exhibit the masking problem
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