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Region-based Voting

Region-based Voting. Query. Exemplar 1. Exemplar 2. 1. Region-based Voting. Query. Exemplar 1. Exemplar 2. 2. Region-based Voting. Mean Shift Clustering. Query. Query. Exemplar 1. Exemplar 2. 3. Discriminative Weight Learning. Not all regions are equally important. D IK. D IJ.

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Region-based Voting

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

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

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

  4. Discriminative Weight Learning • Not all regions are equally important DIK DIJ image K image J exemplar I and diJ=minj χ2(fiI, fjJ) DIJ = Σiwi · diJ want: DIK > DIJ Max-margin formulation results in a sparse solution of weights. Frome, Singer and Malik. NIPS ‘06

  5. Weight Learning Results

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

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

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

  9. ETHZ Shape (Ferrari et al. 06) • Contains 255 images of 5 diverse shape-based classes.

  10. Detection Results on ETHZ 1. Ferrari et al. PAMI 2008. 2. Ferrari, Jurie, Schmid. CVPR 2007

  11. Detection Results on ETHZ

  12. Detection Results on ETHZ

  13. Segmentation Results on ETHZ Orig. Image Segmentation Orig. Image Segmentation The mean average precision is 75.7±3.2%

  14. Segmentation Results on ETHZ Orig. Image Segmentation Orig. Image Segmentation

  15. Segmentation Results on ETHZ Orig. Image Segmentation Orig. Image Segmentation

  16. Segmentation Results on ETHZ Orig. Image Segmentation Orig. Image Segmentation

  17. Segmentation Results on ETHZ Orig. Image Segmentation Orig. Image Segmentation

  18. Complexity Reduction

  19. Caltech 101 results

  20. Context from region tree (ICCV 09)

  21. MSRC dataset

  22. Confusion matrix (mean diagonal 67%)

  23. 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

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