The document discusses computer vision techniques for object detection and localization. It describes methods like selective search that group image regions hierarchically to propose object locations. Large datasets like ImageNet and LabelMe that provide training examples are also discussed. Performance on object detection benchmarks like PASCAL VOC is shown to improve significantly over time. Evaluation standards for concept detection like those used in TRECVID are presented. The document concludes that results are impressively improving each year but that the number of detectable concepts remains limited. It also discusses making feature extraction more efficient using techniques like SURF that take advantage of integral images.

1. 6: Location and context

2. What makes a cow a cow?
Google knows
How do you know? because other people know
We think we know
“because it has four legs”
But the fact of the matter:
not all cows show four legs
nor are they brown …
not all…

3. What is the object in the middle?
No segmentation …
Not even the pixel values of the object …

4. Where is evidence for an object?
Uijlings IJCV 2011

5. Where is evidence for an object?
Uijlings IJCV 2011

6. What is the visual extent of an object?
Uijlings IJCV 2012

7. Where: exhaustive search
Look everywhere for the object window
Imposes computational constraints on
Very many locations and windows
(coarse grid/fixed aspect ratio)
Evaluation cost per location
(weak features/classifiers)
Impressive but takes long.
Viola IJCV 2004 Dalal CVPR 2005
Felzenszwalb PAMI 2010 Vedaldi ICCV 2009 7

8. Where: the need for a hierarchy
An image is intrinsically hierarchical.
Gu CVPR 2009

9. Selective search
Windows formed by hierarchical grouping.
Adjacent grouping on color/texture/shape cues.
Felzenszwalb 2004 Van de Sande ICCV 2011

10. Selective search example

11. Selective search example
11

12. Average best overlap ~88%
… looks like this
High recall
cat

13. Pairs of concepts
Uijlings ICCV demo 2012

14. 6 Conclusion
Selective search gives good localization.
Localization needed to understand pairs of concepts.

15. 7 Data and metadata
http://bit.ly/visualsearchengines

16. How many concepts?
Li Fei Fei slide. Biederman, Psychological Rev. 1987

17. How many examples?
Once you are over 100 – 1000 examples, success is there.

18. Amateur labeling
LabelMe 290,000 object annotations
Russell IJCV 2008

19. Amateur labeling

20. Amateur labeling

21. Tag relevance by social annotation
Consistency in tagging between users on similar images.
Xirong Li, TMM 2009

22. Tag relevance by social annotation
Pretty good for snow not so good for rainbow.

23. Social negative bootstrapping
Negative images are as important as positive images to learn.
Not just random negative images, but close ones.
• We want to learn positive
example from an expert,
and obtain as many
negative samples as we
like for free from the web.
• We iteratively aim for the
hardest negatives.
Xirong Li ACM MM 2009

24. Social negative bootstrapping
Xirong Li ICMR 2011

25. Knowledge ontology ImageNet

26. acknowledgement
WordNet friends
Christiane Fellbaum
Dan Osherson Kai Li Alex Berg Columbia
Princeton Princeton
Jia Deng Hao Su
Princeton/Stanford Stanford

27. PASCAL VOC
The PASCAL Visual Object Classes (VOC).
500,000 Images downloaded from flickr.
Queries like “car”, “vehicle”, “street”, “downtown”.
10,000 objects, 25,000 labels.
Mark Everingham, Luc Van Gool, Chris Williams, John Winn,
Andrew Zisserman

28. 7. Conclusion
Data is king.
The data are beginning to reflect the human cognition
capacity [at a basic level].
Harvesting social data requires advanced computer
vision control.

29. 8 Performance

30. PASCAL 2010
Aeroplane Bicycle Bird Boat Bottle
Bus Car Cat Chair Cow

31. True Positives - Person
UOCTTI_LSVM_MDPM
NLPR_HOGLBP_MC_LCEGCHLC
NUS_HOGLBP_CTX_CLS_RESCORE_V2

32. False Positives - Person
UOCTTI_LSVM_MDPM
NLPR_HOGLBP_MC_LCEGCHLC
NUS_HOGLBP_CTX_CLS_RESCORE_V2

33. Non-birds & non-boats
Non-bird images:
Highest ranked
Non-boat images:
Highest ranked
Water texture and scene composition?

34. Non-chair

35. True Positives - Motorbike
MITUCLA_HIERARCHY
NLPR_HOGLBP_MC_LCEGCHLC
NUS_HOGLBP_CTX_CLS_RESCORE_V2

36. False Positives - Motorbike
MITUCLA_HIERARCHY
NLPR_HOGLBP_MC_LCEGCHLC
NUS_HOGLBP_CTX_CLS_RESCORE_V2

37. Object localization 2008-2010
60
50
Max AP (%)
40
2008
30 2009
2010
20
10
0
tvmonitor
pottedplant
bottle
motorbike
diningtable
horse
sofa
train
person
sheep
aeroplane
bicycle
cow
cat
boat
bus
dog
bird
car
chair
Results on 2008 data improve for 2010 methods for all
categories, by over 100% for some categories.

38. TRECvid evaluation standard

39. Concept detection
Aircraft
Beach
Mountain
People marching
Police/Security
Flower

40. Measuring performance
Set of relevant Set of retrieved
Results items items
1.
2.
• Precision Set of relevant
3.
retrieved items
4.
inverse relationship
Recall
5.

41. UvA-MediaMill@TRECVID
• other systems
Snoek et al, TRECVID 04-10

42. Performance doubled in just 3 years
• 36 concept detectors
Even when
using training
data of different
origin, great
progress.
But the number
of concepts is
still limited.
Snoek & Smeulders, IEEE Computer 2010

43. 8. Conclusion
Impressive results and quickly improving per year.
Very valuable competition.
Best non-classes start to make sense!

44. 9 Speed

45. SURF based on integral images
Introduced by Viola & Jones in the context of face
detection: sliding windows in left to right / up to bottom
integral images.
46

46. SURF principle
Approximate Gaussian derivatives with box filters:
Lyy
Lyy
Lyy Lxy
L xx L
LREC 2004, 26 May yy Lisbon
2004, L xy 47

47. SURF speed
Scale
Computation time: 6 times faster than DoG (~100msec).
Independent of filter scale. 26 May 2004, Lisbon
LREC 2004, 48

48. Dense descriptor extraction
Pixel-wise Responses Final Descriptor
Factor 16 speed improvement,
Another factor 2 by the use of matrix libs.

49. Projection: Random Forest
Binary decision trees
.
...
.
...
......
Moosmann et al. 2008

50. Real-time bag of words
Descriptor Projection Classification
Extraction
Pre-projection Actual projection SVM kernel
D-SURF Random MAP:
<empty> RBF
2x2 Forest 0.370
15 10 13
Total computation time is 38 milliseconds per image
26 frames per second on a normal PC in any 20 concepts.

51. 9. Conclusion
SURF scale and rotation invariant
Fast due to the use of integral images
Download: http://www.vision.ee.ethz.ch/~surf/
DURF extraction is 6x faster than Dense-SIFT.
Projection using Random Forest 50x faster than NN.

52. Internet Video Search: the beginning
telling
stories
measuring concept lexicon
video features detection learning
browsing
video
video