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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
Region-based Voting

Query

Exemplar 1

Exemplar 2

1

region based voting1
Region-based Voting

Query

Exemplar 1

Exemplar 2

2

region based voting2
Region-based Voting

Mean

Shift

Clustering

Query

Query

Exemplar 1

Exemplar 2

3

discriminative weight learning
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

algorithm pipeline
Algorithm Pipeline

Exemplars

Images

Ground truths

Detection

Verification classifier

Weight learning

Region matching based voting

Constrained segmenter

Query

Initial Hypotheses

Segmentation

6

initial object background labels
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

propagate object background labels
Propagate Object/Background Labels

Initial Labels

Final Segmentation

Arbelaez and Cohen. CVPR 08

8

ethz shape ferrari et al 06
ETHZ Shape (Ferrari et al. 06)
  • Contains 255 images of 5 diverse shape-based classes.
detection results on ethz
Detection Results on ETHZ

1. Ferrari et al. PAMI 2008. 2. Ferrari, Jurie, Schmid. CVPR 2007

segmentation results on ethz
Segmentation Results on ETHZ

Orig. Image

Segmentation

Orig. Image

Segmentation

The mean average precision is 75.7±3.2%

segmentation results on ethz1
Segmentation Results on ETHZ

Orig. Image

Segmentation

Orig. Image

Segmentation

segmentation results on ethz2
Segmentation Results on ETHZ

Orig. Image

Segmentation

Orig. Image

Segmentation

segmentation results on ethz3
Segmentation Results on ETHZ

Orig. Image

Segmentation

Orig. Image

Segmentation

segmentation results on ethz4
Segmentation Results on ETHZ

Orig. Image

Segmentation

Orig. Image

Segmentation

concluding remarks
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