A joint presentation of a ECCV'12 and a CVPR'13

1. From Unsupervised to
Semi-Supervised Event Detection
Wen-Sheng Chu
Robotics Institute, Carnegie Mellon University
July 9, 2013
1
Jeffery CohnFernando De la Torre

2. Outline
1. Unsupervised Temporal Commonality
Discovery
(Chu et al, ECCV’12)
2. Personalized Facial Action Unit Detection
(Chu et al, CVPR’13)
2

3. Unsupervised Commonality Discovery
in Images
Where are the repeated patterns?
3
(Chu’10, Mukherjee’11, Collins’12)

4. Unsupervised Commonality Discovery
in Videos?
• We name it Temporal Commonality Discovery (TCD).
• Goal: Given two videos, discover common events in
an unsupervised fashion. 4

5. TCD is hard!
1) No prior knowledge on commonalities
– We do not know what, where and how many
commonalities exist in the video
2) Exhaustive search are computationally prohibitive
– E.g., two videos with 300 frames have >8,000,000,000
possible matches.
possible locations possible lengths
possibilities/sequence
Another possibilities/sequence
5

6. Formulation
6
Integer programming!

7. Optimization: Interpretation
7

8. Optimization: Native Search
Complexity 8

9. Optimization: Branch-and-Bound
• Similar to the idea of ESS (Lampert’08), we search the
space by splitting intervals.
9

10. Optimization: Branch-and-Bound
• Bounding histogram bins
10

11. 1. Bounding L1 distance:
2. Intersection similarity:
3. X2 distance:
Optimization: Branch-and-Bound
11

12. Unlikely
search
regions
(B1,E1,B2,E2; -10)
Searching Structure
(B1,E1,B2,E2; 32)
Priority queue
(sorted by bound scores)
…
(B1,E1,B2,E2; -50)
(B1,E1,B2,E2; -105)
State S = (Rectangle set; score)
12

13. (B1,E1,B2,E2; -105)
Algorithm
(B1,E1,B2,E2; 32)
Priority queue
(sorted by bound scores)
…
(B1,E1,B2,E2; -50)
(B1,E1,B2,E2; -105)
Top state
1. Pop out
the top state
2. Split
13

14. (B1,E1,B2,E2; -105)
Algorithm
(B1,E1,B2,E2; 32)
Priority queue
(sorted by bound scores)
…
(B1,E1,B2,E2; -50)
Top state
(B1,E’1,B2,E2; -76)
(B1,E’’1,B2,E2; -61)
3. Compute
bounding scores
4. Push back the
split states
14

15. Algorithm
(B1,E1,B2,E2; 32)
Priority queue
(sorted by bound scores)
…
(B1,E1,B2,E2; -50)
Top state
(B1,E’1,B2,E2; -76)
(B1,E’’1,B2,E2; -61)
• The algorithm stop when
the top state contains an
unique rectangle.
Omit most of the
search space with
large distances
15

16. Compare with Relevant Work
1. Difference between TCD and ESS
[1]/STBB[2]
– Different learning framework:
• Unsupervised v.s. Supervised
– New bounding functions for TCD
2. Difference between TCD and [3]
– Different objective:
• Commonality Discovery v.s. Temporal Clustering
[1] “Efficient subwindow search: A branch and bound framework for object
localization”, PAMI 2009.
[2] “Discriminative video pattern search for efficient action detection”, PAMI 2011. 16

17. Experiment (1): Synthesized Sequence
Histograms of the discovered
pair of subsequences
17

18. Experiment (2):
Discover Common Facial Actions
• RU-FACS dataset*
– Interview videos with 29 subjects
– 5000~8000 frames/video
– Collect 100 segments that containing smiley mouths (AU-
12)
– Evaluate in terms of averaged precision
18
* “Automatic recognition of facial actions in spontaneous expressions”, Journal of
Multimedia 2006.

19. Experiment (2):
Discover Common Facial Actions
19

20. • Parametric settings for Sliding Windows (SW)
• Log of #evaluations:
• Quality of discovered patterns:
• a
Experiment (2): Speed Evaluation
Speed #evaluation of the distance function´
log
nT C D
nSW i
d(r SW i
) ¡ d(r T C D
)
20

21. Experiment (2):
Discover Common Facial Actions
• Compare with LCCS* on -distance
21
* “Frame-level temporal calibration of unsynchronized cameras by using Longest
Consecutive Common Subsequence”, ICASSP 2009.

22. Experiment (3): Discover
Multiple Common Human Motions
• CMU-Mocap dataset:
– http://mocap.cs.cmu.edu/
• 15 sequences from Subject 86
• 1200~2600 frames and up to 10 actions/seq
• Exclude the comparison with SW because it
needs >1012 evaluations
22

23. Experiment (3): Discover
Multiple Common Human Motions
23

24. Experiment (3): Discover
Multiple Common Human Motions
• Compare with LCCS* on -distance
24

25. Extension: Video Indexing
• Goal: Given a query , find the best common
subsequence in the target video
• A straightforward extension:
Temporal
Search
Space
25

26. A Prototype for Video Indexing
26

27. Summary
27

28. Questions?
[1+ “Common Visual Pattern Discovery via Spatially Coherent
Correspondences,” In CVPR 2010.
[2+ “MOMI-cosegmentation: simultaneous segmentation of multiple objects
among multiple images,” In ACCV 2010.
[3+ “Scale invariant cosegmentation for image groups,” In CVPR 2011.
[4+ “Random walks based multi-image segmentation: Quasiconvexity results
and GPU-based solutions,” In CVPR 2012.
[5+ “Frame-level temporal calibration of unsynchronized cameras by using
Longest Consecutive Common Subsequence,” In ICASSP 2009.
[6+ “Efficient ESS with submodular score functions,” In CVPR 2011.
28
http://humansensing.cs.cmu.edu/wschu/

29. Outline
1. Unsupervised Temporal Commonality
Discovery
(Chu et al, ECCV’12)
2. Selective Transfer Machine for Personalized
Facial Action Unit Detection
(Chu et al, CVPR’13)
29

30. AU 6+12
Facial Action Units (AU)
30

31. Main Idea
31

32. Related Work: Features
32

33. Related Work: Classifiers
33

34. Feature Bias
Person specific!
34

35. Occurrence Bias
35

36. Selective Transfer Machine (STM)
Formulation
Maximizes margin of penalized SVM
Minimize distribution mismatch
36

37. Goal (1): Maximize penalized SVM margin
margin
penalized loss
37

38. Goal (2): Minimize Distribution Mismatch
• Kernel Mean Matching (KMM)*
38
* “Covariate shift by kernel mean matching”, Dataset shift in machine learning, 2009.

39. Goal (2): Minimize Distribution Mismatch
Groundtruth
Bad estimator
for testing data!
39

40. Better fitting!
Groundtruth
Selection by reweighting
training data
40
Goal (2): Minimize Distribution Mismatch

41. 41

42. 42
Optimization: Alternate Convex Search

43. 43
Optimization: Alternative Convex Search

44. Compare with Relevant Work
44
[1] "Covariate shift by kernel mean matching," Dataset shift in machine
learning, 2009.
[2] "Transductive inference for text classification using support vector
machines," In ICML 1999.
[3] "Domain adaptation problems: A DASVM classification technique and a
circular validation strategy," PAMI 2010.

45. Experiments
• Features
– SIFT descriptors on 49 facial landmarks
– Preserve 98% energy using PCA
45
Datasets #Subjects #Videos #Frm/vid Content
CK+ 123 593 ~20 NeutralPeak
GEMEP-FERA 7 87 20~60 Acting
RU-FACS 29 29 5000~7500 Interview

46. Experiment (1): Synthetic Data
46

47. • Two protocols
– PS1: train/test are separate data of the same subject
– PS2: training subjects include test subject (same protocol in [2])
• GEMEP-FERA
Experiment (2): Comparison with Person-
specific (PS) Classifiers
47

48. Experiment (2): Selection Ability of STM
48

49. • 123 subjects, 597 videos, ~20 frames/video
Experiment (3): CK+
49

50. Experiment (4): GEMEP-FERA
50
• 7 subjects, 87 videos, 20~60 frames/video

51. • 29 subjects, 29 videos, 5000~7000 frames/vid
Experiment (5): RU-FACS
51

52. Summary
• Person-specific biases exist among face-
related problems, esp. facial expression
• We propose to alleviate the biases by
personalizing classifiers using STM
• Next
– Joint optimization in terms of
– Reduce the memory cost using SMO
– Explore more potential biases in face problems,
e.g., occurrence bias
52

53. Questions?
[1] "Covariate shift by kernel mean matching," Dataset shift in machine
learning, 2009.
[2] "Transductive inference for text classification using support vector
machines," In ICML 1999.
[3] "Domain adaptation problems: A DASVM classification technique and a
circular validation strategy," PAMI 2010.
*4+ “Integrating structured biological data by kernel maximum mean
discrepancy”, Bioinformatics 2006.
*5+ “Meta-analysis of the first facial expression recognition challenge,” IEEE
Trans. on Systems, Man, and Cybernetics, Part B, 2012.
53
http://humansensing.cs.cmu.edu/wschu/