1. Unsupervised learning of models for recognition
P. Perona, M. Weber, and M. Welling
California Institute of Technology,
Universita’ di Padova &
University College, London
Vision Sciences 2001

2. Meet the xyz

3. Meet the xyz

4. Meet the xyz

5. Meet the xyz

6. Meet the xyz

7. Meet the xyz

8. Spot the xyz

9. Spot the xyz

10. Spot the xyz

11. Spot the xyz

12. Spot the xyz

13. Spot the xyz

14. Main issues:
Representation
Recognition
Learning
ARVO 1999

15. Variability within a category
Intrinsic
Deformation

16. Model: constellation of Parts
Tanaka et al., 1993
Fischler & Elschlager, 1973
Yuille, ‘91
Brunelli & Poggio, ‘93
Lades, v.d. Malsburg et al. ‘93
Lanitis, Taylor et al. ‘95
Amit & Geman, ‘95, ‘99
Burl, Leung, Perona ‘95, ‘96, ‘98
Perrett & Oram, 1993

17. A
Deformations

18. A B
Deformations

19. A B
Deformations C

20. A B
Deformations C D

21. Presence / Absence of Features
occlusion

22. Background clutter

23. Generative probabilistic model
Model (Parameters)
Foreground pdf
Prob. of Detection
Background pdf
0.8
0.9
Prob. of N detect.
Pdf of location
pPoisson(N1|1)
0.9
pPoisson(N2|2)
p(x)=A-1 (uniform)
Final Image
pPoisson(N3|3)
e.g. p(x)=G(x| , )
Example
1. Object Part Positions
2. Part Absence
3a. N false detect
3b. Position f. detect N1 N2 N3

24. Optimal observer detection+ + + + + + + + + + + + + + + + + + +
Likelihood ratio test
[From Burl, Weber & Perona - CVPR 1996]

25. Unsupervised learning : Frontal Views of Faces
200 Images (100 training, 100 testing)
30 people, different for training and testing

26. Unsupervised detector training - 1
Highly textured points are detected with Förstner’s interest operator.
can detect corner points and circular patterns
produces more than 10,000 patterns

27. Unsupervised detector training - 2
“Pattern Space” (100+ dimensions)

28. Parts in model

29. Parameter Estimation
Apply detectors…..
Obtain training images, suppose we had some detectors

30. optimize for representation
Parameter Estimation
Chicken-and-egg problem with shape
and correspondence. Use EM.
Signal? Clutter? Correspondence?
optimize for representation
(ML on generative models)

31. ML using EM 1. Current estimate
2. Assign probabilities to constellations (E)
Large P
...
pdf
Image 1
Image 2
Image i
Small P
3. Use probabilities as weights to reestimate parameters (M). Example: 
Large P x +
Small P x
+ … =
new estimate of 

32. Learned face model Preselected Parts Test Error: 6% (4 Parts)
Parts in Model
Model Foreground pdf
Sample Detection

33. Face images (~90% correct classific.)

34. Background images

35. Rear Views of Cars 200 Images (100 training, 100 testing)
Only one image per car
High-pass filtered

36. Learned Model Preselected Parts Test Error: 13% (5 Parts)
Parts in Model
Model Foreground pdf
Sample Detection

37. Detections of Cars (~87% correct classification)

38. Background Images

39. Dilbert
125 examples
77 examples
vs.

40. Dilbert Model Model Foreground pdf Preselected Parts Parts in Model
Sample Detection
Test Error: 15% (4 Parts)

41. Main points Probabilistic constellation models (ARVO ’99)
Maximum Likelihood Learning
Unsupervised learning of object categories in clutter
Works well on faces, heads, cars, Dilbert, leaves, handwriting

42. Pietro Perona, Markus Weber, Max Welling References
FG 2000 (viewpoint invariance)
ECCV 2000 (EM algor. for unsupervised learning)
CVPR 2000 (learning of multiple classes)
available from:
(click on `publications’)
Funded by:
National Science Foundation
Sloan Foundation
Pietro Perona,
Markus Weber,
Max Welling