Recognition using Regions (Demo)

1. Recognition using Regions (Demo) Sudheendra V

2. Outline • Generating multiple segmentations • Normalized cuts [Ren & Malik (2003)] • Uniform regions • Watershed transform [Arbel´aez1et al. (2009)] • Non-uniform • Multiple scales • Discovering object regions using LDA • ground truth segments • multiple segmentation • single segmentation • hierarchical segmentation

3. Multiple Segmentations • Normalized cuts • segmentation as graph partitioning • nodes -> pixels, edge between neighboring pixels • edge weight -> affinity between pixels • partition graph into K components • parameters • number of partitions K • properties • similar sized partitions (normalized) • preserves region boundaries for large enough K • multiple segmentations • vary number of partitions K • resize image to different resolutions http://www.cs.sfu.ca/~mori/research/superpixels/ affinity matrix

4. Normalized cuts (examples) K = 4 K = 6 K = 7

5. Normalized cuts (examples) K = 3 K = 5 K = 7 • Extra edge • “normalized” regions

6. Normalized cuts (examples) K = 3 K = 5 K = 7

7. Multiple Segmentations • Watershed transform • contours are detected using texture, edge cues and oriented watershed transform used to determine contour scale • parameters • thresholding scale for contours, k • properties • variable sized regions • preserves region boundaries • multiple segmentations • vary thresholding scale k • resize image to different resolutions http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/ Contours at multiple scales Threshold at a scale

8. Watershed (examples) K = 200 K = 180 K = 160 Threshold at different contour scales K to generate multiple segmentations

9. Watershed (examples) K = 195 K = 183 K = 169 Threshold at different contour scales K to generate multiple segmentations

10. Watershed (Hierarchical segmentation) K = 190 K = 175 K = 140 K = 155 Thresholding scales in an increasing sequence produces a hierarchical segmentation

11. Watershed (Hierarchical segmentation) K = 200 K = 175 K = 145 K = 155 Thresholding scales in an increasing sequence produces a hierarchical segmentation

12. Ncuts vs Watershed Ncuts Watershed Comparison of multiple segmentations generated using Ncuts vs Watershed

13. Ncuts vs Watershed Ncuts Watershed Comparison of multiple segmentations generated using Ncuts vs Watershed

14. Outline • Generating multiple segmentations • Normalized cuts [Ren & Malik (2003)] • Uniform regions • Watershed transform [Arbel´aez1et al. (2009)] • Non-uniform • Multiple scales • Discovering object regions using LDA • ground truth segments • multiple segmentation • single segmentation • hierarchical segmentation

15. Multiple Segmentations

16. Discovering object regions using LDA • Approach • Parameters – number of topics to discover Generate multiple segmentations Use LDA to discover topics based on word co-occurrence Bag of words rep for each segment Extract local features (SIFT) Rank segments based on similarity to topic

17. Dataset Note that the paper uses a larger set containing ~ 4000 images (MSRC_v0) • MSRC_v2 dataset • 23 categories • 591 images • 1648 objects Distribution of categories in MSRC_v2

18. Implementation details • Dense sift on edge points and 3 different scales • 2000 visual words • 8 segmentations using different parameters • ~ 40k segments in total • LDA takes ~ 10 mins

19. Ground Truth Segments • ground truth segments are directly used • number of topics set to 25 (~ num categories) Top 20 segments in terms of similarity of word distribution to a topic

20. Ground Truth Segments Number of topics = 25

21. Ground Truth Segments Number of topics = 50

22. Ground Truth Segments Number of topics = 75

23. Ground Truth Segments • Quantitative results Overlap score on top 20 segments Average precision (area under precision-recall curve)

24. Multiple segmentations • Normalized cuts • k = {3, 5, 7, 9} • 2 resolutions

25. Multiple segmentations

26. Multiple segmentations Multiple segmentations vs. Ground truth

27. Multiple segmentations • Quantitative results Overlap score on top 20 segments Average precision (area under precision-recall curve)

28. Multiple segmentations • Effect of number of images • Overlap score on top 20 segments • topics are easier to discover with more object instances

29. Single segmentation Multiple segmentations vs. Single segmentation Single segmentation returns partial objects for some classes

30. Single segmentation Multiple segmentations vs. Single segmentation Single segmentation returns partial objects for some classes

31. Single segmentation Multiple segmentations vs. Single segmentation

32. Single/Multiple segmentations • Quantitative results Overlap score on top 20 segments Average precision (area under precision-recall curve)

33. Hierarchical segmentation Hierarchical segmentation vs. Multiple segmentation (Ncuts) Watershed threshold set such there are 12 leaf nodes and entire hierarchical tree is used by LDA

34. Hierarchical segmentation Hierarchical segmentation vs. Multiple segmentation (Ncuts)

35. Hierarchical segmentation Hierarchical segmentation vs. Multiple segmentation (Ncuts)

36. Hierarchical segmentation • Quantitative results Overlap score on top 20 segments • Contour-based watershed method • does better for objects with few internal contours (grass, sky) • is worse for objects with large number of contours (flower, airplane)

37. Conclusion • Generating multiple segmentations • ncuts and watershed provide different tradeoffs • bottom-up segmentation needs different parameters for different objects • Discovering objects using LDA • number of topics matters quite a bit • topics are easier to discover with more examples • multiple segmentation does better than because different objects require different parameters • contour-based watershed method does better for objects with few internal contours