1. Recognition by finding patterns
We have seen very simple template matching (under filters)
Some objects behave like quite simple templates
Frontal faces
Strategy:
Find image windows
Correct lighting
Pass them to a statistical test (a classifier) that accepts faces and rejects non-faces
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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2. Basic ideas in classifiers
Loss
some errors may be more expensive than others
e.g. a fatal disease that is easily cured by a cheap medicine with no side-effects -> false positives in diagnosis are better than false negatives
We discuss two class classification: L(1->2) is the loss caused by calling 1 a 2
Total risk of using classifier s
Computer Vision - A Modern Approach
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3. Basic ideas in classifiers
Generally, we should classify as 1 if the expected loss of classifying as 1 is better than for 2
gives
Crucial notion: Decision boundary
points where the loss is the same for either case
1 if
2 if
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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4. Some loss may be inevitable: the minimum
risk (shaded area) is called the Bayes risk
Computer Vision - A Modern Approach
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5. Finding a decision boundary is not the same as
modelling a conditional density.
Computer Vision - A Modern Approach
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6. Example: known distributions
Assume normal class densities, p-dimensional measurements with common (known) covariance and different (known) means
Class priors are
Can ignore a common factor in posteriors - important; posteriors are then:
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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7. Classifier boils down to: choose class that minimizes:
Mahalanobis
distance
because covariance is common, this simplifies to sign of
a linear expression:
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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8. Plug-in classifiers
Assume that distributions have some parametric form - now estimate the parameters from the data.
Common:
assume a normal distribution with shared covariance, different means; us usual estimates
ditto, but different covariances; ditto
Issue: parameter estimates that are “good” may not give optimal classifiers.
picture from Ripley
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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9. Histogram based classifiers
Use a histogram to represent the class-conditional densities
(i.e. p(x|1), p(x|2), etc)
Advantage: estimates become quite good with enough data!
Disadvantage: Histogram becomes big with high dimension
but maybe we can assume feature independence?
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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10. Finding skin
Skin has a very small range of (intensity independent) colours, and little texture
Compute an intensity-independent colour measure, check if colour is in this range, check if there is little texture (median filter)
See this as a classifier - we can set up the tests by hand, or learn them.
get class conditional densities (histograms), priors from data (counting)
Classifier is
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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11. Figure from “Statistical color models with application to skin detection,” M.J. Jones and J. Rehg, Proc. Computer Vision and Pattern Recognition, 1999 copyright 1999, IEEE
Computer Vision - A Modern Approach
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12. Figure 22.4. It’s quite hard to see much difference between 1, 2, and 3
Figure from “Statistical color models with application to skin detection,” M.J. Jones and J. Rehg, Proc. Computer Vision and Pattern Recognition, 1999 copyright 1999, IEEE
Computer Vision - A Modern Approach
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13. Finding faces
Faces “look like” templates (at least when they’re frontal).
General strategy:
search image windows at a range of scales
Correct for illumination
Present corrected window to classifier
Issues
How corrected?
What features?
What classifier?
what about lateral views?
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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14. Naive Bayes (Important: naive not necessarily perjorative)
Find faces by vector quantizing image patches, then computing a histogram of patch types within a face
Histogram doesn’t work when there are too many features
features are the patch types
assume they’re independent and cross fingers
reduction in degrees of freedom
very effective for face finders
why? probably because the examples that would present real problems aren’t frequent.
Many face finders on the face detection home page
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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15. Figure from A Statistical Method for 3D Object Detection Applied to Faces and Cars, H. Schneiderman and T. Kanade, Proc. Computer Vision and Pattern Recognition, 2000, copyright 2000, IEEE
Computer Vision - A Modern Approach
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16. Face Recognition Whose face is this? (perhaps in a mugshot) Issue:
What differences are important and what not?
Reduce the dimension of the images, while maintaining the “important” differences.
One strategy:
Principal components analysis
Many face recognition strategies at
Computer Vision - A Modern Approach
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17. prove this on the whiteboard
Main point is that the first principal component is the direction of largest variance. I usually
prove this on the whiteboard
Computer Vision - A Modern Approach
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18. Figure 22.6 - story in the caption
Computer Vision - A Modern Approach
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19. Difficulties with PCA Projection may suppress important detail
smallest variance directions may not be unimportant
Method does not take discriminative task into account
typically, we wish to compute features that allow good discrimination
not the same as largest variance
Computer Vision - A Modern Approach
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20. Computer Vision - A Modern Approach
Fig Principal components will give a very poor repn of this data set
Computer Vision - A Modern Approach
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21. Computer Vision - A Modern Approach
Two classes indicated by * and o; the first principal component captures all the variance,
but completely destroys any ability to discriminate. The second is close to what’s required.
Computer Vision - A Modern Approach
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22. Linear Discriminant Analysis
We wish to choose linear functions of the features that allow good discrimination.
Assume class-conditional covariances are the same
Want linear feature that maximises the spread of class means for a fixed within-class variance
I do the two lines of math on the whiteboard - it’s easier to explain notation, etc. that way.
Section
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23. Computer Vision - A Modern Approach
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24. The first linear discriminant gets excellent separation
Computer Vision - A Modern Approach
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25. Computer Vision - A Modern Approach
Figure read the caption. This yields quite a good illustration of the power of the method.
Computer Vision - A Modern Approach
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26. Neural networks Linear decision boundaries are useful
but often not very powerful
we seek an easy way to get more complex boundaries
Compose linear decision boundaries
i.e. have several linear classifiers, and apply a classifier to their output
a nuisance, because sign(ax+by+cz) etc. isn’t differentiable.
use a smooth “squashing function” in place of sign.
Computer Vision - A Modern Approach
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27. Computer Vision - A Modern Approach
Here phi is a squashing function; this is figure We seek to adjust the w’s to get
the best classifier.
Computer Vision - A Modern Approach
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28. 22.14 - a bunch of different squashing functions
Computer Vision - A Modern Approach
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29. Training Choose parameters to minimize error on training set
Stochastic gradient descent, computing gradient using trick (backpropagation, aka the chain rule)
Stop when error is low, and hasn’t changed much
Computer Vision - A Modern Approach
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30. The vertical face-finding part of Rowley, Baluja and Kanade’s system
Figure from “Rotation invariant neural-network based face detection,” H.A. Rowley, S. Baluja and T. Kanade, Proc. Computer Vision and Pattern Recognition, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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31. Histogram equalisation gives an approximate fix for illumination induced variability
Histogram equalisation (fig 22.15)
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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32. Architecture of the complete system: they use another neural
net to estimate orientation of the face, then rectify it. They
search over scales to find bigger/smaller faces.
Always worth asking: “why is it better to search over scales than to use bigger templates?”
Figure from “Rotation invariant neural-network based face detection,” H.A. Rowley, S. Baluja and T. Kanade, Proc. Computer Vision and Pattern Recognition, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
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33. Figure from “Rotation invariant neural-network based face detection,” H.A. Rowley, S. Baluja and T. Kanade, Proc. Computer Vision and Pattern Recognition, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
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34. Convolutional neural networks
Template matching using NN classifiers seems to work
Natural features are filter outputs
probably, spots and bars, as in texture
but why not learn the filter kernels, too?
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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35. subsample, and finally classify based on outputs of this process.
A convolutional neural network, LeNet; the layers filter, subsample, filter,
subsample, and finally classify based on outputs of this process.
Figure from “Gradient-Based Learning Applied to Document Recognition”, Y. Lecun et al Proc. IEEE, 1998 copyright 1998, IEEE
Computer Vision - A Modern Approach
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36. LeNet is used to classify handwritten digits. Notice that the
test error rate is not the same as the training error rate, because
the test set consists of items not in the training set. Not all
classification schemes necessarily have small test error when they
have small training error.
22.19
Figure from “Gradient-Based Learning Applied to Document Recognition”, Y. Lecun et al Proc. IEEE, 1998 copyright 1998, IEEE
Computer Vision - A Modern Approach
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37. Support Vector Machines
Neural nets try to build a model of the posterior, p(k|x)
Instead, try to obtain the decision boundary directly
potentially easier, because we need to encode only the geometry of the boundary, not any irrelevant wiggles in the posterior.
Not all points affect the decision boundary
Computer Vision - A Modern Approach
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38. Computer Vision - A Modern Approach
Two point sets that are in fact separable by a linear classifier boundary. Notice
that relatively few points determine the boundary (a subset of each side’s hull).
Computer Vision - A Modern Approach
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39. Support Vector Machines
Linearly separable data means
Choice of hyperplane means
Hence distance
I do only linearly separable cases in class, and don’t derive the dual problem.
Computer Vision - A Modern Approach
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40. Support Vector Machines
Actually, we construct a dual optimization problem.
By being clever about what x means, I can have much
more interesting boundaries.
Computer Vision - A Modern Approach
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41. Space in which decision boundary is linear - a
conic in the original space
has the form
An SVM in a non-linear space. I don’t do the Mercer representation of this process; this
figure, etc. usually gives enough illumination to the process.
Computer Vision - A Modern Approach
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42. Vision applications Reliable, simple classifier,
use it wherever you need a classifier
Commonly used for face finding
Pedestrian finding
many pedestrians look like lollipops (hands at sides, torso wider than legs) most of the time
classify image regions, searching over scales
But what are the features?
Compute wavelet coefficients for pedestrian windows, average over pedestrians. If the average is different from zero, probably strongly associated with pedestrian
Computer Vision - A Modern Approach
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43. Fig 22.21 - caption tells the story
Figure from, “A general framework for object detection,” by C. Papageorgiou, M. Oren and T. Poggio, Proc. Int. Conf. Computer Vision, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
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44. Figure 22.22 - caption tells the story
Figure from, “A general framework for object detection,” by C. Papageorgiou, M. Oren and T. Poggio, Proc. Int. Conf. Computer Vision, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
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45. Figure 22.23 - caption tells the story
Figure from, “A general framework for object detection,” by C. Papageorgiou, M. Oren and T. Poggio, Proc. Int. Conf. Computer Vision, 1998, copyright 1998, IEEE
Computer Vision - A Modern Approach
Set: Recognition as Template Matching
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