1. Biased Normalized Cuts 1 Subhransu Maji and Jithndra Malik University of California, Berkeley IEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR 2011) Poster Nisheeth K. Vishnoi Miscrosoft Research India, Bangalore

2. Outline Review of ‘Normalized Cuts’ Introduction to ‘Biased Normalized Cuts’ Biased Graph Partitioning Algorithm Qualitative Evaluation 2

3. Review of Normalized Cuts 3 Shi and Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 Goal: minimize the cut value

4. Review of Normalized Cuts 4

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7. Introduction to ‘Biased Normalized Cuts’ 7 Incorporate the both bottom-up and top-down work \ Bottom-up: detect contours corresponding to significant changes in brightness, color or texture Top-down: look for strong activation

8. Introduction to ‘Biased Normalized Cuts’ 8

9. 9 Two advantages: 1.Solutions which are sufficiently “correlated” with priors which allows us to use noisy top-down information 2.Given the spectral solution of the unconstrained problem, the solution of the constrained one can be computed in small additional time, which allows to run the algorithm in an interactive mode

10. Weighted Graph and Normalized Cuts 10

11. Weighted Graph and Normalized Cuts 11

12. The Spectral Relaxation to Computing Normalized Cuts 12

13. Biased Normalized Cuts 13

14. Biased Normalized Cuts 14 Solve the normalized cut problem The prior information

15. Algorithm 15 26 in the paper

16. Qualitative Evaluation 16 Use the PASCAL VOC 2010 dataset Weight matrix: use the intervening contour cue [M. Maire et al., Using contours to detect and localize junctions in natural images, CVPR, 2008]

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18. Result 18 Pb: probability of boundary map [M. Maire et al., Using contours to detect and localize junctions in natural images, CVPR, 2008]

19. Result 19 Use the probability estimates by an object detector as a bias The object detector used as a seed vector [L. Bourdev et al., Detecting people using mutually consistent poselet activations, ECCV, 2010]