Analysis of Current Segmentation Methods

1. Analysis of Current Segmentation Methods Jacob D’Avy

2. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

3. Graph Based Method • Source: -Efficient Graph-Based Image SegmentationPedro F.Felzenszwalb, Daniel P. Huttenlocher International Journal of Computer Vision, Volume 59, Number 2, September 2004 • Premise: -An image is represented as a graph where: • vertices = pixels • edges = connections between pixels • weights = measure of dissimilarity between pixels -Components are made of connected pixels. -The goal is to cut edges between ‘dissimilar’ components.

4. Difference Measures Internal Difference: largest weight in the Min Spanning Tree of component Difference Between: minimum weight connecting two components Threshold: Min Internal Difference: Efficient Graph-Based Image Segmentation Pedro F.Felzenszwalb, Daniel P. Huttenlocher International Journal of Computer Vision, Volume 59, Number 2, September 2004

5. Algorithm • Sort edges by increasing weight • For each edge E(v1,v2) • if (v1 and v2 are from different components) • if (w(E) <= Mint(C(v1), C(v2))) • merge(C(v1),C(v2)) The decision to merge is based on whether the difference between two components is small relative to the difference within the components. Efficient Graph-Based Image Segmentation Pedro F.Felzenszwalb, Daniel P. Huttenlocher International Journal of Computer Vision, Volume 59, Number 2, September 2004

6. Example For E(AB): Evaluate: Merge(A,B)

7. Example For E(A,C): Evaluate: Don’t Merge(AB,C)

8. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

9. Publication Output σ=0.8 K=300 min=20 input output Parameters: σ=std deviation of Gaussian filter K=threshold parameter min=minimum component size zoom of output Efficient Graph-Based Image Segmentation Pedro F.Felzenszwalb, Daniel P. Huttenlocher International Journal of Computer Vision, Volume 59, Number 2, September 2004

10. Basic Image Parameters: sigma=0.5 K=300 min=20 input output

11. Results sigma=0.8, K=300, min=20 sigma=0.8, K=300, min=20 Segmentation Methods for Multiple Body Parts, Sitapa Rujikietgumjorn

12. Varying K Parameters: sigma=0.8 K=50-500 min=20

13. Varying min Parameters: sigma=0.8 K=500 min=20-400

14. Graph Based Method Summary Decide if there is evidence for a boundary between two components based on the difference between them compared to the difference within. If there is not then merge the two components.

15. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

16. N cuts Paper: Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik Premise: graph based segmentation weights based on similarity partitions are made by removing connecting edges goal is to make cuts between most dissimilar components normalization of the cuts reduces bias toward small isolated sets

17. N cuts Method Cut: where w() is a measure of similarity (larger w=more similar) Partition: the optimal partition will minimize the cut value Normalized Cut: the total connection from nodes in A to all nodes in the graph Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik

18. N cuts Method The minimization can be performed using eigenvalues Affinity Matrix: Degree Matrix: The eigenvector that corresponds to the 2nd smallest eigenvalue is used to partition the graph. Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik Segmentation Using Eigenvectors Carlos Vallespi

19. N cuts Example Find eigenvectors using: Use eigenvector corresponding to the second smallest eigenvalue

20. N cuts Example Reshape eigenvector to m x n: Discretize based on sign (or avg) : Partition the graph :

21. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

22. N cuts Basic Image input output nSegs=9 nSegs=14

23. N cuts Results nSegs=14 nSegs=14 Segmentation Methods for Multiple Body Parts, Sitapa Rujikietgumjorn

24. N cuts Varying nSegs nSegs=2-40

25. Ncuts Summary Goal is to make cuts along a path that has the least similarity. Eigenvalues of the affinity matrix are used to find the min cut. Graph is repeatedly partitioned until the desired number of segments is left.

26. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

27. Mean Shift Paper: Mean Shift: A Robust Approach Toward Feature Space Analysis Premise: Map the image into a feature space Use mean shift to find the most dense clusters These clusters become a palette to which all pixels are assigned

28. Mean Shift Method Find the mean location of the data in the window Move the center of the search window to the mean data location

29. Mean Shift Segmentation Method • Convert the image into a feature space • Choose initial search windows uniformly in the space • Adjust window using mean shift until a convergence is reached • All pixels that passed through a window are assigned to that cluster

30. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

31. Mean Shift Basic Image input output hs=7 hr=6.5 min=20

32. Mean Shift Results hs=7 hr=6.5 min=200 hs=7 hr=6.5 min=300 Segmentation Methods for Multiple Body Parts, Sitapa Rujikietgumjorn

33. Mean Shift Varying hs hs=1-20

34. Mean Shift Varying hr hr=1-20

35. Mean Shift Varying min min=20-970

36. Mean Shift Summary Finds most dense clusters in feature space Clusters become components of segmentation Effective method for many applications

37. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

38. Active Contour Paper: Active Contours Without Edges Tony F. Chan, and Luminita A. Vese Premise: Contour is an energy function Forces act on the contour until it reaches an equilibrium Equilibrium exists when the contour is equal to the boundary of the object

39. Active Contour Method Define an image: and a curve: Define a fitting term:

40. Active Contour Example

41. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

42. Active Contour Basic Image input output 250 iterations

43. Active Contour Basic Image

44. Active Contour Results 250 iterations Segmentation Methods for Multiple Body Parts, Sitapa Rujikietgumjorn

45. Active Contour Input Initialization 150 iterations

46. Active Contour Output 250 iterations Input Initialization Initialization is important for the final segmentation result.

47. Outline • Introduction • Graph based method • description • results • N-cuts • description • results • Mean shift • description • results • Active contour • description • results • Conclusion

48. Conclusion Four segmentation methods presented. Different approaches. Effective in different applications

49. References • Efficient Graph-Based Image Segmentation Pedro F. Felzenszwalb, Daniel P. Huttenlocher • Normalized Cuts and Image Segmentation Jianbo Shi and Jitendra Malik • Mean Shift: A Robust Approach Toward Feature Space Analysis Dorin Comaniciu, Peter Meer • Active Contours Without Edges Tony F. Chan, and Luminita A. Vese • http://www.cc.gatech.edu/classes/cs7322_97_spring/participants/Sumner/discussions/snakes.html • http://www.shawnlankton.com/2007/05/active-contours/ • Synergism in Low Level VisionChristopher M. Christoudias, Bogdan Georgescu, Peter Meer • Segmentation and Grouping Gary Bradski Sebastian Thrun http://robots.stanford.edu/cs223b/index.html • Segmentation of Color Images using Mean Shift Algorithm for Feature Extraction M.V. Sudhamani , Dr.C.R. venugopal