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Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Boundary Extraction in Natural Images Using Ultrametric Contour Maps. Pablo Arbel á ez Universit é Paris Dauphine Presented by Derek Hoiem. What is segmentation?. What is segmentation?. Segmentation is a result. Face. Woman. What is segmentation?. Segmentation is a result

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Boundary Extraction in Natural Images Using Ultrametric Contour Maps

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  1. Boundary Extraction in Natural Images Using Ultrametric Contour Maps Pablo Arbeláez Université Paris Dauphine Presented by Derek Hoiem

  2. What is segmentation?

  3. What is segmentation? • Segmentation is a result

  4. Face Woman What is segmentation? • Segmentation is a result • Segmentation is a process

  5. What is segmentation? • Segmentation is a result • Segmentation is a process • Segmentation is a guide

  6. Segmentation as a Guide • Multiple Segmentations

  7. Segmentation as a Guide • Multiple Segmentations • Hierarchy of Segmentations

  8. Key Concepts/Contributions • Hierarchical segmentation by iterative merging • Ultrametric dissimilarities • Thorough evaluation on BSDS

  9. λ Hierarchical Segmentation 3 Region Image Dendrogram Contour Image

  10. λ Ultrametric Contour Map • Ultrametric • Definition: D(x,y) <= max{ D(x,z), D(z,y) } The union R12 of two regions R1 and R2 must have >= distance to adjacent region R3 than either R1or R2

  11. Ultrametric Contour Map

  12. Region Dissimilarity • Dc(R1, R2): mean boundary contrast • contrast(x) = max L*a*b* diff within radius of x • Dg(R1, R2): mean boundary gradient • gradient(x) = Pb(x) • Da(R1): Area + α3 Scatter (in color space) α2 D(R1, R2) = [Dc(R1, R2) + α1 Dg(R1, R2)] · min{ Da(R1), Da(R2)} Learned Parameters: xi = 4.5 α1 = 5 α2 = 0.2 α3 = 0

  13. Examples Contrast Contrast + Gradient Contrast + Gradient + Region

  14. Algorithm Summary • Create Initial Contours: • Extrema in gray channel form regions • Assign pixels to regions based on above ultrametric • Iteratively merge regions • Keep adjacency/distance matrix

  15. Comparison • Martin et al. (Pb) • Canny edge detector • Hierarchical watersheds (using MFM for gradient) [Najman and Schmitt 1996] • Variational (global energy minimization)

  16. Pb Brightness Gradient Oriented Edges Color Gradient Texture Gradient No Boundary Boundary [Martin Fowlkes Malik 2004]

  17. Pb

  18. Variational Method Originally Wavelet-based Textons [Koepfler Lopez Morel 1994]

  19. Comparison • MFM: Martin et al. (Pb) • Canny: Canny edge detector • WS: Hierarchical watersheds (using MFM for gradient) [Najman and Schmitt 1996] • MS: Variational (global energy minimization) Edge-Based Region-Based

  20. Comparison

  21. Results

  22. Results

  23. Best Results http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

  24. Best Results http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

  25. Best Results http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

  26. Best Results http://www.ceremade.dauphine.fr/~arbelaez/results-UCM/main.html

  27. Median Results

  28. Median Results

  29. Median Results

  30. Median Results

  31. Worst Results

  32. Worst Results

  33. Worst Results

  34. Worst Results

  35. Hierarchies vs. Multiple Segmentations

  36. Revising Segmentation

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