1 / 37

Normalized Cuts Demo

Normalized Cuts Demo. Original Implementation from: Jianbo Shi Jitendra Malik Presented by: Joseph Djugash. Outline. Clustering Point The Eigenvectors The Affinity Matrix Comparison with K-means Segmentation of Images The Eigenvectors Comparison with K-means.

louie
Télécharger la présentation

Normalized Cuts Demo

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Normalized Cuts Demo Original Implementation from: Jianbo Shi Jitendra Malik Presented by: Joseph Djugash

  2. Outline • Clustering Point • The Eigenvectors • The Affinity Matrix • Comparison with K-means • Segmentation of Images • The Eigenvectors • Comparison with K-means

  3. Clustering – How many groups are there? Out of the various possible partitions, which is the correct one?

  4. Clustering – Why is it hard? • Number of components/clusters? • The structure of the components? • Estimation or optimization problem? • Convergence to the globally correct solution?

  5. Clustering – Example 1 Optimal? How do we arrive at this Clustering?

  6. What does the Affinity Matrix Look Like?

  7. The Eigenvectors and the Clusters Step-Function like behavior preferred! Makes Clustering Easier.

  8. The Eigenvectors and the Clusters

  9. Dense Square Cluster Sparse Square Cluster Sparse Circle Cluster Clustering – Example 2

  10. Normalized Cut Result

  11. The Affinity Matrix

  12. The Eigenvectors and the Clusters

  13. e2 e1 K-means – Why not? Affinity Matrix NCut Output Input K-means Clustering? Eigenvectors Possible but not Investigated Here. K-means Output Eigenvector Projection

  14. K-means Result – Example 1

  15. K-means Result – Example 2

  16. Varying the Number of Clusters N-Cut K-means k = 3 k = 4 k = 6

  17. Varying the Sigma Value σ = 3 σ = 13 σ = 25

  18. Image Segmentation – Example 1 Affinity/Similarity matrix (W) based on Intervening Contours and Image Intensity

  19. The Eigenvectors

  20. Comparison with K-means Normalized Cuts K-means Segmentation

  21. How many Segments?

  22. Good Segmentation (k=6,8)

  23. Bad Edge Missing Edge Bad Segmentation (k=5,6) • Choice of # of Segments in Critical. • But Hard to decide without prior knowledge.

  24. Varying Sigma –(σ= Too Large)

  25. Varying Sigma –(σ= Too Small) • Choice of Sigma is important. • Brute-force search is not Efficient. • The choice is also specific to particular images.

  26. Image Segmentation – Example 2

  27. Image Segmentation – Example 2 Normalized Cuts K-means Segmentation

  28. Image Segmentation – Example 3

  29. Image Segmentation – Example 3 Normalized Cuts K-means Segmentation

  30. Image Segmentation – Example 4

  31. Image Segmentation – Example 4 Normalized Cuts K-means Segmentation

  32. Image Segmentation – Example 5

  33. Image Segmentation – Example 5 Normalized Cuts K-means Segmentation

  34. Image Segmentation – Example 6

  35. Comparison with K-means Normalized Cuts K-means Segmentation

  36. The End…

  37. The Eigenvectors and the Clusters Eigenvector #2 Eigenvector #3 Eigenvector #5 Eigenvector #4 Eigenvector #1

More Related