ImageNet Auto-Annotation with Segmentation Propagation
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1 ImageNet Auto-Annotation with Segmentation Propagation Matthieu Guillaumin Daniel Küttel Vittorio Ferrari Bryan Anenberg & Michela Meister
2 Outline Goal & Motivation System Overview Segmentation Transfer Joint Segmentation Results
3 Goal Automatic foreground pixel-level segmentation of ImageNet
4 ImageNet large-scale, hierarchical 15,000,000 images 22,000 classes
5 Outline Goal & Motivation System Overview Segmentation Transfer Joint Segmentation Results
6 System Overview source S transfer segmentation joint segmentation unsegmented T segmented T new source = S U T [3] Guillamin, Kuettel, Ferrari
7 slide credit: V. Ferrari
8 slide credit: V. Ferrari
9 slide credit: V. Ferrari
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11 slide credit: V. Ferrari
12 slide credit: V. Ferrari
13 slide credit: V. Ferrari
14 slide credit: V. Ferrari
15 slide credit: V. Ferrari
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17 slide credit: V. Ferrari
18 slide credit: V. Ferrari
19 Outline Goal & Motivation System Overview Segmentation Transfer Joint Segmentation Results
20 slide credit: V. Ferrari
21 slide credit: V. Ferrari
22 slide credit: V. Ferrari
23 slide credit: V. Ferrari
24 slide credit: V. Ferrari
25 slide credit: V. Ferrari
26
27 slide credit: V. Ferrari
28 Segmentation Transfer [3]
29 Outline Goal & Motivation System Overview Segmentation Transfer Joint Segmentation Results
![[4] Batra](/docs-images/88/117486437/images/30-0.jpg "Joint")
30 [4] Batra Joint Segmentation [5] Rother
31 Joint Segmentation with Shared Appearance slide credit: V. Ferrari
32 Joint Segmentation with Shared Appearance
33 Joint Segmentation with Shared Appearance
34 Joint Segmentation with Shared Appearance
35 Joint Segmentation with Shared Appearance 1. Appearance model for image i.
36 Joint Segmentation with Shared Appearance 1. Appearance model for image i. 2. Appearance model for class C
37 Joint Segmentation with Shared Appearance 1. Appearance model for image i. 2. Appearance model for class C 3. Transferred mask from source S to image i
38 Joint Segmentation with Shared Appearance 3. Transferred mask from source S to image i
39 Joint Segmentation with Shared Appearance 1. Appearance model for image i. 2. Appearance model for class C 3. Transferred mask from source S to image i
40 Joint Segmentation with Shared Appearance 4. Appearance model for related classes
41 Outline Goal & Motivation System Overview Segmentation Transfer Joint Segmentation Results
42 slide credit: V. Ferrari
43 Experiments on ImageNet animal, instruments subtrees 60k bounding boxes 440k only class labels 4k manually annotated over 450 classes
44 slide credit: V. Ferrari
45 slide credit: V. Ferrari
46 slide credit: V. Ferrari
47 Conclusion automatic large-scale exploits class structure extends segmentation datasets
48 References [1] A. Rosenfeld and D. Weinshall. Extracting Foreground Masks towards Object Recognition. In Proceedings IEEE International Conference on Computer Vision, [2] D. Kuettel and V. Ferrari. Figure-ground segmentation by transferring window masks. Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on p [3] M. Guillamin, D. Kuettel, V. Ferrari. ImageNet Auto-Annotation with Segmentation Propagation. International Journal of Computer Vision [4] Batra, D.; Kowdle, A.; Parikh, D.; Jiebo Luo; Tsuhan Chen, "icoseg: Interactive co-segmentation with intelligent scribble guidance," Computer Vision and Pattern Recognition (CVPR), 2010 [5] Rother, C.; Minka, T.; Blake, A.; Kolmogorov, V., "Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs," Computer Vision and Pattern Recognition, 2006
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