A Paper Presentation for "Structured Forests for Fast Edge Detection" by Dollár, Piotr, and C. Lawrence Zitnick at Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE, 2013.

2. Dollár, Piotr, and C. Lawrence Zitnick. "Structured forests for fast edge detection.“ Computer Vision (ICCV), 2013 IEEE International Conference on. IEEE, 2013.

3. MainContribution
Compute edge maps in realtime,
faster than the competing state-of-the-art
Proposed
Method
Structured Random Forests
This presentation is inspired by the talk: http://techtalks.tv/talks/structured-forest-for-fast-edge-detection/59412/

4. Edge Definition
Source: http://upload.wikimedia.org/wikipedia/en/8/8e/EdgeDetectionMathematica.png

5. Edge Definition
Source: http://upload.wikimedia.org/wikipedia/en/8/8e/EdgeDetectionMathematica.png

6. Where this work excelsAccuracy& Speed[realtime]

7. Where this work excelsAccuracy& Speed[realtime]

8. Where this work excelsAccuracy& Speed[realtime]

9. Where this work excelsAccuracy& Speed[realtime]

10. Edge Detection asClassification Problem

11. Edge Detection as Classification Problem
• {0, 1}

12. Edge Detection as Classification Problem
• {0, 1}
•Binaryclassification ignoring the local structures of the edges

13. Edges have Structures

14. Clustering Sketch Tokens
Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection, Joseph J. Lim et al. 2013

15. Random Forests

16. Random Forests
ℎ푥,휃=푥푘1−푥푘2<휏

17. Random Forests

18. Random Forests

19. Random Forests

20. Random Forests For Edge Detection

21. Random Forests For Edge Detection

22. Random Forests For Edge Detection

23. Random Forests For Edge Detection

24. Random Forests For Edge Detection

25. Random Forests For Edge Detection
Decision:

26. Structured Random Forests

27. The Output Space
{0, 1}2
{,….}151
Dimensionality
Input Space

28. The Output Space
{0, 1}2
{,….}151
2256
Dimensionality
Input Space

29. Node Split
Low entropy split

30. Training Model
Bad split

31. Training Model
Good split

32. Training Model
Cluster the structured labels

33. Training Model
Just one difference to random forests:
clusterthe output into a binary or multiclass output using distance function

34. Clustering
푌: Structured space where information gain not well defined
퐶: Discrete space where information space is good defined
푍: Intermediate space where similarity measurement is easy to compute
Π∶푌→푍, 푍→퐶

35. Training Model
•Computing information gain
–Labels 퐶are discrete, standard entropy criterions used.
•Combining predictions
–To combine 푦1…푦푛∈푌into a prediction:
•Compute푧푖=Π휑(푦푖)of dimension 푚
•Select 푦푘, whose 푧푘=푎푟푔푚푖푛푧푘 푖,푗(푧푘푗−푧푖푗)2(medoid)
+ Computing medoids is fast, 푂(푛푚)

36. Training Structured Forests For Edge Detection

37. Training Structured Forests For Edge Detection
32x32 RGB image patch →7228 features

38. Training Structured Forests For Edge Detection
32x32 RGB image patch →7228 features
Π∶푌→푍
Dimension of 푍=2562
Down-sampled to m = 256

39. Training Structured Forests For Edge Detection
32x32 RGB image patch →7228 features
Π∶푌→푍
Dimension of 푍=2562
Down-sampled to m = 256

40. Edge Detection with Structured Forests
32x32 RGB image patch →7228 features

41. Edge Detection with Structured Forests
32x32 RGB image patch →7228 features
푌is a 16x16 segmentation mask

42. Multi-scale Detection

43. Multi-scale Detection

44. Multi-scale Detection

45. Results
•BSDS 500 image set
–Multi-scale ties or outperforms the accuracy of the state of the art.
–Single-scale improves runtime by 5x to 10x

46. Results
•BSDS 500 image set
–Multi-scale ties or outperforms the accuracy of the state of the art.
–Single-scale improves runtime by 5x to 10x

47. Results
•BSDS 500 image set
–Multi-scale ties or outperforms the accuracy of the state of the art.
–Single-scale improves runtime by 5x to 10x

48. Results
•NYU image set
–Multi-scale is slightly better than the state of the art.
–Improved performance by multiple orders of magnitude

49. Conclusions
•Realtimestructured learning method for edge detection
•General purpose method for learning structured random forests
•Real time + state of the art accuracy → new applications possible
•Novel learning approach may be applicable to other problems.

50. Thank you