Learning to Combine Bottom-Up and Top-Down Segmentation

1. Learning to Combine Bottom-Up and Top-Down Segmentation Anat Levin and Yair Weiss School of CS&Eng, The Hebrew University of Jerusalem, Israel

2. Bottom-up segmentation Bottom-up approaches:Use low level cues to group similar pixels • Malik et al, 2000 • Sharon et al, 2001 • Comaniciu and Meer, 2002 • …

3. Bottom-up segmentation is ill posed Many possible segmentation are equally good based on low level cues alone. Some segmentation example (maybe horses from Eran’s paper) images from Borenstein and Ullman 02

4. Top-down segmentation • Class-specific, top-down segmentation (Borenstein & Ullman Eccv02) • Winn and Jojic 05 • Leibe et al 04 • Yuille and Hallinan 02. • Liu and Sclaroff 01 • Yu and Shi 03

5. Combining top-down and bottom-up segmentation + • Find a segmentation: • Similar to the top-down model • Aligns with image edges

6. Previous approaches • Borenstein et al 04 Combining top-down and bottom up segmentation. • Tu et al ICCV03 Image parsing: segmentation, detection, and recognition. • Kumar et al CVPR05 Obj-Cut. • Shotton et al ECCV06: TextonBoost Previous approaches: Train top-down and bottom-up modelsindependently

7. Why learning top-down and bottom-up models simultaneously? • Large number of freedom degrees in tentacles configuration- requires a complex deformable top down model • On the other hand: rather uniform colors- low level segmentation is easy

8. Our approach • Learn top-down and bottom-up models simultaneously • Reduces at run time to energy minimization with binary labels (graph min cut)

9. Energy model Segmentation alignment with image edges Consistency with fragments segmentation

10. Energy model Segmentation alignment with image edges Consistency with fragments segmentation

11. Resulting min-cut segmentation Energy model Segmentation alignment with image edges Consistency with fragments segmentation

12. Learning from segmented class images Training data: Goal: Learn fragments for an energy function

13. Learning energy functions using conditional random fields • Theory of CRFs: • Lafferty et al 2001 • LeCun and Huang 2005 • CRFs For vision: • Kumar and Hebert 2003 • Ren et al 2006 • He et al 2004, 2006 • Quattoni et al 2005 • Torralba et al 04

14. Maximize energy of all other configurations Minimize energy of true segmentation E(x) E(x) Learning energy functions using conditional random fields “It's not enough to succeed. Others must fail.” –Gore Vidal

15. Maximize energy of all other configurations Minimize energy of true segmentation P(x) P(x) Learning energy functions using conditional random fields “It's not enough to succeed. Others must fail.” –Gore Vidal

16. Differentiating CRFs log-likelihood Log-likelihood is convex with respect to Log-likelihood gradients with respect to : Expected feature responseminusobserved feature response Yair- in the original version of this slide I had another equation expressing the expectation as a sum of marginals (see next hidden slide). At least for me, it wasn’t originally clear what this expectation means before I saw the other equation. However, I try to delete un necessary equations..

17. Conditional random fields-computational challenges • CRFscost- evaluating partition function • Derivatives- evaluating marginal probabilities • Use approximate estimations: • Sampling • Belief Propagation and Bethe free energy • Used in this work: Tree reweighted belief propagation and Tree reweighted upper bound (Wainwright et al 03)

18. Greedy energy design: Fragments selection Candidate fragments pool:

19. Fragments selection challenges Straightforward computation of likelihood improvement is impractical 2000 Fragments 50 Training images 10 Fragments selection iterations 1,000,000inference operations!

20. Fragment with low error on the training set Fragments selection First order approximation to log-likelihood gain: Fragment not accounted for by the existing model • Similar idea in different contexts: • Zhu et al 1997 • Lafferty et al 2004 • McCallum 2003

21. Fragments selection First order approximation to log-likelihood gain: • Requires a single inference process on the previous iteration energy to evaluate approximations with respect to all fragments • First order approximation evaluation is linear in the fragment size

22. Fragments selection- summary • Initialization: Low- level term • For k=1:K • Run TRBP inference using the previous iteration energy. • Approximate likelihood gain of candidate fragments • Add to energy the fragment with maximal gain.

23. Training horses model

24. Training horses model-one fragment

25. Training horses model-two fragments

26. Training horses model-three fragments

27. Results- horses dataset

28. Results- horses dataset Mislabeled pixels percent Fragments number Comparable to previous results (Kumar et al, Borenstein et al.) but with far fewer fragments

29. Results- artificial octopi

30. Results- cows dataset From the TU Darmstadt Database

31. Results- cows dataset Mislabeled pixels percent Fragments number

32. Conclusions • Simultaneously learning top-down and bottom-up segmentation cues. • Learning formulated as estimation in Conditional Random Fields • Novel, efficient fragments selection algorithm • Algorithm achieves state of the art performance with a significantly smaller number of fragments