1. Supervised Learning of Edges and Object Boundaries Piotr Dollár Zhuowen Tu Serge Belongie

2. The problem

3. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results

4. Outline I. Motivation –Why edges? –Why not edges? –Why learning? II. Problem formulation III. Learning architecture ( BEL ) IV. Results

5. Why edges? Reduce dimensionality of data Preserve content information Useful in applications such as: –object detection –structure from motion –tracking

6. Why not edges? But, not that useful, why? Difficulties: 1.Modeling assumptions 2.Parameters 3.Multiple sources of information (brightness, color, texture, …) 4.Real world conditions Is edge detection even well defined?

7. Canny edge detection 1. smooth 2. gradient 3. thresh, suppress, link Canny is optimal w.r.t. some model.

8. Canny edge detection 1. smooth 2. gradient 3. thresh, suppress, link And yet…

9. 1.Modeling assumptions Step edges, junctions, etc. 2.Parameters Scales, threshold, etc. 3.Multiple sources of information Only handles brightness 4.Real world conditions Gaussian iid noise? Texture… Canny difficulties

10. 1.Modeling assumptions Complex models, computationally prohibitive 2.Parameters Many, may use learning to help tune 3.Multiple sources of information Typically brightness, color, and texture cues 4.Real world conditions Aimed at real images Modern methods

11. Modern methods ( Pb ) Pb – Martin et al. PAMI04

12. 1.Modeling assumptions minimal 2.Parameters none 3.Multiple sources of information Automatically incorporated 4.Real world conditions training data Why learning?

13. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results

14. Problem formulation (general) image scene interpretation that can include spatial location and extent of objects, regions, object boundaries, curves, etc. 0/1 function that encodes spatial extent of a component of W Obtaining optimal or likely W or S W can be difficult. Let: We seek to learn this distribution directly from image data. To further reduce complexity, we can discard the absolute coordinates of S: where N(c) is the neighborhood of I centered at c.

15. Problem formulation (edges) image segmentation 1 on boundaries of segments, 0 elsewhere

16. Discriminative framework Sample positive and negative patches according to above: Given an image I and n interpretations W obtained by manual annotation, we can compute: Goal is to learn from human labeled images Finally train a classifier!

17. Edge point present in center? NOYES Discriminative framework

18. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results

19. Learning architecture Large training set O(10 8 ) –but correlated –very variable data Want generic, efficient features –applicability to any domain –fast computation essential Boosting a natural choice

20. AdaBoost Taken from tutorial by Jiri Matas and Jan Sochman

21. Decision Stumps Weak learners: (where f is some feature of x)

22. AdaBoost (decision stumps)

23. Cascaded classifiers Minimize computation during testing Especially useful for skewed prior Viola-Jones face/pedestrian detection

24. Cascade (AdaBoost)

25. Probabilistic boosting trees Expected amount of computation decreases significantly Once a mistake is made, it cannot be undone Cascade also made problem easier! Ideally, splitting data creates two sub-problems each much easier than original…

26. Probabilistic boosting trees

27. Retain efficiency of cascades Add power when necessary Prone to overfitting Tree was necessary to obtain good results. … … …

28. Haar features: Feature response: (image response to green squares) – (image response to red squares) Applied to many ‘views’ of the data –grayscale, color, Gabor filter outputs, etc. –at many orientations, locations, etc Fast computation using integral images Hundreds of thousands of candidate features …

29. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results –Gestalt laws –Natural images –Road detection –Object Boundaries

30. Results Boosted edge learning ( BEL ) Compare to method with best known performance ( Pb ), and also to Canny Comparison not quite fair… Pb – Martin et al. PAMI04

31. Gestalt laws Gestalt laws of perceptual organization –Symmetry, closure, parallelism, etc. –Govern how component parts are organized into overall patterns The “hard” part of edge detection What can and cannot be achieved in our framework?

32. Analogies A:B :: C : ?

33. Gestalt laws: parallelism

34. Gestalt laws: modal completion

35. Gestalt laws: alternate interpretation

36. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results –Gestalt laws –Natural images –Road detection –Object Boundaries

37. Natural Images Berkeley Segmentation Dataset and Benchmark –Standard dataset for edge detection with 300 manually annotated images –Modern benchmark for comparing edge detection algorithms Notes: –Edge detection in natural images is hard –Possibly ill-defined problem –Evil but necessary comparison

38. Natural Images: results

40. image human BELPb Natural Images: probabilities

41. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results –Gestalt laws –Natural images –Road detection –Object Boundaries

42. Road detection location of roads in scene 1 if pixel is on the road, 0 elsewhere Road detection is not edge detection But same learning architecture Ground truth obtained from map data

43. Road detection (training) (the 2 training images)

44. Road detection (testing) (the testing image) (`Winchester Dr.’ was not detected)

45. Outline I. Motivation II. Problem formulation III. Learning architecture ( BEL ) IV. Results –Gestalt laws –Natural images –Road detection –Object Boundaries

46. Object boundaries location and extent of object of interest 1 on boundaries of object, 0 elsewhere Must tune to specific ‘type’ of edge Algorithms that model edges not applicable Potentially most useful application

47. Object boundaries (context)

48. Object boundaries (training) …

49. Object boundaries (ground truth)

50. Object boundaries (Canny) F-score =.10

51. Object boundaries ( Pb ) F-score =.13

52. Object boundaries ( BEL ) F-score =.79

53. Algorithm roundup AccurateAdaptableFast Canny Pb BEL

54. Summary Define edges only in terms of labeled data, minimal modeling assumptions Minimize human effort in adapting algorithm to particular domain Fast, affordable edge detection for the masses!

55. Thank you!