1. Region-based Voting Exemplar 1 Query 1 Exemplar 2

2. Region-based Voting Query 2 Exemplar 1 Exemplar 2

3. Region-based Voting Query Mean Shift Clustering 3 Exemplar 1 Exemplar 2

4. Computer Vision Group UC Berkeley Discriminative Weight Learning Not all regions are equally important Frome, Singer and Malik. NIPS ‘06 image J exemplar I image K want: D IJ D IK D IK > D IJ Max-margin formulation results in a sparse solution of weights. D IJ = Σ i w i · d i J and d i J =min j χ 2 (f i I, f j J )

5. Computer Vision Group UC Berkeley Weight Learning Results

6. Algorithm Pipeline Region matching based voting Verification classifier Constrained segmenter Query Exemplars Images Ground truths Initial HypothesesSegmentation Detection Weight learning 6

7. Initial Object/Background Labels Initial Labels Exemplar 7 Transformed Mask QueryMatched Part : Object label : Background label : Unknown label + Fully automatic unlike interactive use of Graph Cuts, e.g. Blake et al. ECCV 04

8. Propagate Object/Background Labels 8 Arbelaez and Cohen. CVPR 08 Initial LabelsFinal Segmentation

9. Computer Vision Group UC Berkeley ETHZ Shape (Ferrari et al. 06) Contains 255 images of 5 diverse shape-based classes.

10. Computer Vision Group UC Berkeley Detection Results on ETHZ Hough baseline 1 kAS 1 Shape 2 Ours Det. rate at 0.3FPPI31.0%62.4%67.2%87.1±2.8% 1. Ferrari et al. PAMI 2008. 2. Ferrari, Jurie, Schmid. CVPR 2007

11. Computer Vision Group UC Berkeley Detection Results on ETHZ

12. Computer Vision Group UC Berkeley Detection Results on ETHZ

13. Computer Vision Group UC Berkeley Segmentation Results on ETHZ Orig. ImageSegmentation The mean average precision is 75.7±3.2% Orig. ImageSegmentation

14. Computer Vision Group UC Berkeley Segmentation Results on ETHZ Orig. ImageSegmentation Orig. ImageSegmentation

15. Computer Vision Group UC Berkeley Segmentation Results on ETHZ Orig. ImageSegmentation Orig. ImageSegmentation

16. Computer Vision Group UC Berkeley Segmentation Results on ETHZ Orig. ImageSegmentation Orig. ImageSegmentation

17. Computer Vision Group UC Berkeley Segmentation Results on ETHZ Orig. ImageSegmentation Orig. ImageSegmentation

18. Computer Vision Group UC Berkeley Complexity Reduction

19. Computer Vision Group UC Berkeley Caltech 101 results

20. Computer Vision Group UC Berkeley Context from region tree (ICCV 09)

21. Computer Vision Group UC Berkeley MSRC dataset

22. Computer Vision Group UC Berkeley Confusion matrix (mean diagonal 67%)

23. Computer Vision Group UC Berkeley Concluding Remarks Our approach –Bottom up region segmentation –Hough transform style voting (learned weights) –Top down segmentation –Capture context by region tree Results on ETHZ, Caltech 101, MSRC competitive Lot more needs to be done to produce a robust solution to the problem of combining top down and bottom up information, but I think this is the central problem of vision