1. 1 P. Arbelaez, M. Maire, C. Fowlkes, J. Malik. Contour Detection and Hierarchical image Segmentation. IEEE Trans. on PAMI, 2011. Student: Hsin-Min Cheng Advisor: Sheng-Jyh Wang

2. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Results  Conclusion 2

3. Introduction Original ImageContour  Contour 3

4. Introduction Original ImageSegmentation  Segmentation 4

5. Introduction  From Contour to Segmentation Original ImageSegmentationContour 5

6. Introduction  Goal  Contour Detection  Hierarchical Segmentation from Contours Original ImageSegmentationContour 6

7. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Results  Conclusion 7

8. Contour Detection 1. Learn local boundary cues 2. Global framework to capture closure, continuity 3. Local Cues and global cues combination 8

9.  Learn local boundary cues Image Local Boundary Cues Model Brightness Color Texture Cue Combination Contour Detection 9

10.  Learn local boundary cues  Brightness  L*a*b* colorspace  Color  L*a*b* colorspace  Texture  Convolve with 17 filters Filters for creating textons 10 Contour Detection

11. 11  Learn local boundary cues  Oriented gradient of histograms  Example  Gradient magnitude G at location(x, y)  Three scales of r 11 Contour Detection ure

12. 12  Learn local boundary cues  Local Cues Combination 12 Contour Detection ure

13.  Global framework to capture closure, continuity Contour Detection 13 V:image pixels E:connections between pairs of nearby pixels =>Build a weighted graph G=(V,E) from image

14.  Global framework to capture closure, continuity Contour Detection 14

15.  Local Cues and global cues combination Contour Detection 15 Local CuesGlobal cues

16. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Results  Conclusion 16

17. Hierarchical Segmentation  Multiple Segmentations  Fixed resolution  Hierarchy of Segmentations  Flexible resolution adjustment 17

18. Hierarchical Segmentation 1. From contours to segmentation 2. Hierarchical segmentation by iterative merging 18

19. Hierarchical Segmentation  From contours to segmentation  Watershed Transform  Concept 19

20. Hierarchical Segmentation  From contours to segmentation  Watershed Transform  Example 20

21. Hierarchical Segmentation  From contours to segmentation  Watershed Transform 21 Boundary strength Artifacts Weight each arc

22. Hierarchical Segmentation  From contours to segmentation  Oriented Watershed Transform 22 WT OWT

23. Hierarchical Segmentation  Hierarchical segmentation by iterative merging  Hierarchical segmentation  Example 23

24. Brief Summary 24 Original Image - Local cues - Global cues Oriented Gradient of histograms Contour Oriented Watershed Transform Iterative Merging Hierarchical Segmentation

25. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Results  Conclusion 25

26. Result 26

27. Result 27

28. Result 28 Evaluation of segmentation algorithmsEvaluation of contour detector  BSDS300 Dataset

29. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Results  Conclusion 29

30. Conclusion  A high performance contour detector, combining local and global image information  A method to transform any contour detector signal into a hierarchy of regions while preserving contour quality 30

31. Reference  P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. Contour Detection and Hierarchical Image Segmentation. IEEE TPAMI, Vol. 33, No. 5, pp. 898-916, May 2011  P. Arbelaez, M. Maire, C. Fowlkes and J. Malik. From Contours to Regions: An Empirical Evaluation. In CVPR 2009.  P. Arbelaez and L. Cohen. Constrained Image Segmentation from Hierarchical Boundaries. In CVPR 2008. 31

32. Outline  Introduction  Contour Detection  Hierarchical Segmentation  Evaluation  Results 32

33. Boundary Benchmarks  ODS : optimal dataset scale  OIS : optimal image scale  AP :average precision 33

34. Region benchmarks(1)  Segment Covering  Probabilistic Rand Index [Unnikrishnan et. al. 07] [Yang et. al. 08]  Variation of Information [Meila 05] Distance Between two segmentations in terms of their average conditional entropy given by 34

35. Region benchmarks(2) CoveringRand Index Variation of Information 35

36. Additional Dataset 36