Biased Normalized Cuts

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Biased Normalized Cuts. IEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR 2011 ) Poster. Subhransu Maji and Jithndra Malik University of California, Berkeley. Nisheeth K. Vishnoi Miscrosoft Research India, Bangalore. Outline. Review of ‘ Normalized Cuts’ - PowerPoint PPT Presentation

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Biased Normalized Cuts1Subhransu Maji and Jithndra MalikUniversity of California, BerkeleyIEEE Conference on Computer Vision and Pattern Recognition (IEEE CVPR 2011)Poster

Nisheeth K. VishnoiMiscrosoft Research India, BangaloreOutlineReview of Normalized Cuts

Introduction to Biased Normalized Cuts

Biased Graph Partitioning

Algorithm

Qualitative Evaluation

2Review of Normalized Cuts3

Shi and Malik, Normalized Cuts and Image Segmentation, PAMI, 2000 Goal: minimize the cut valueReview of Normalized Cuts4Review of Normalized Cuts5Review of Normalized Cuts6

Introduction to Biased Normalized Cuts7Incorporate 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

Introduction to Biased Normalized Cuts8

Introduction to Biased Normalized Cuts9Two advantages:

Solutions which are sufficiently correlated with priors which allows us to use noisy top-down information

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

Weighted Graph and Normalized Cuts 10Weighted Graph and Normalized Cuts11The Spectral Relaxation to Computing Normalized Cuts 12Biased Normalized Cuts13Biased Normalized Cuts14

Solve the normalized cut problemThe prior information Algorithm15

26 in the paper

Qualitative Evaluation16Use 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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Result18Pb: probability of boundary map [M. Maire et al., Using contours to detect and localize junctions in natural images, CVPR, 2008]

Result19Use 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]