Download - Region-based Voting
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Region-based Voting
Exemplar 1
Query
1
Exemplar 2
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Region-based Voting
Query
2
Exemplar 1
Exemplar 2
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Region-based Voting
Query Query
MeanShift
Clustering
3
Exemplar 1
Exemplar 2
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Computer Vision GroupUC Berkeley
Discriminative Weight Learning
• Not all regions are equally important
Frome, Singer and Malik. NIPS ‘06
image J exemplar I image K
want:
DIJ DIK
DIK > DIJMax-margin formulation results in a sparse solution of weights.
DIJ = Σi wi · diJand di
J=minj χ2(fiI, fj
J)
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Computer Vision GroupUC Berkeley
Weight Learning Results
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Algorithm Pipeline
Region matching based voting
Verification classifier
Constrained segmenter
Query
Exemplars
Images
Ground truths
Initial Hypotheses Segmentation
Detection
Weight learning
6
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Initial Object/Background Labels
Initial Labels
Exemplar
7
Transformed Mask
Query Matched Part
: Object label: Background label: Unknown label
+
Fully automatic unlike interactive use of Graph Cuts, e.g. Blake et al. ECCV 04
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Propagate Object/Background Labels
8
Arbelaez and Cohen. CVPR 08Initial Labels Final Segmentation
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Computer Vision GroupUC Berkeley
ETHZ Shape (Ferrari et al. 06)• Contains 255 images of 5 diverse shape-based
classes.
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Computer Vision GroupUC Berkeley
Detection Results on ETHZ
Hough baseline1 kAS 1 Shape 2 Ours
Det. rate at 0.3FPPI 31.0% 62.4% 67.2% 87.1±2.8%
1. Ferrari et al. PAMI 2008. 2. Ferrari, Jurie, Schmid. CVPR 2007
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Computer Vision GroupUC Berkeley
Detection Results on ETHZ
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Computer Vision GroupUC Berkeley
Detection Results on ETHZ
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Computer Vision GroupUC Berkeley
Segmentation Results on ETHZ
Orig. Image Segmentation
The mean average precision is 75.7±3.2%
Orig. Image Segmentation
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Computer Vision GroupUC Berkeley
Segmentation Results on ETHZ
Orig. Image Segmentation Orig. Image Segmentation
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Computer Vision GroupUC Berkeley
Segmentation Results on ETHZ
Orig. Image Segmentation Orig. Image Segmentation
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Computer Vision GroupUC Berkeley
Segmentation Results on ETHZ
Orig. Image Segmentation Orig. Image Segmentation
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Computer Vision GroupUC Berkeley
Segmentation Results on ETHZ
Orig. Image Segmentation Orig. Image Segmentation
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Computer Vision GroupUC Berkeley
Complexity Reduction
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Computer Vision GroupUC Berkeley
Caltech 101 results
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Computer Vision GroupUC Berkeley
Context from region tree (ICCV 09)
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Computer Vision GroupUC Berkeley
MSRC dataset
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Computer Vision GroupUC Berkeley
Confusion matrix (mean diagonal 67%)
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Computer Vision GroupUC 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