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[1] P. Arbel´ aez, M. Maire, C. Fowlkes, and J. Ma- lik. Contour Detection and Hierarchical Image Segmentation. PAMI, 2011. [2]M. Maire, S. X. Yu, and P. Perona. Object De- tection and Segmentation from Joint Embedding of Parts and Pixels. ICCV, 2011. [3] D. Martin, C. Fowlkes, D. Tal, and J. Malik. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. ICCV, 2001. [4] M. Spain and P. Perona. Measuring and Predict- ing Object Importance. IJCV, 2010. ONR MURI N00014-10-1-0933 and ARO/JPL-NASA Stennis NAS7.03001 supported this work. Part of Stella Yu’s work was supported by NSF CAREER IIS-1257700. Benchmarks Image Groundtruth UCM gPb-UCM [1] Residual Construct groundtruth Ultrametric Contour Map (UCM): Weight each boundary by level at which it appears in object-part hierarchy Determine boundary visibility by region tree traversal Does machine hierarchical segmentation [1] respect object-part containment? Residuals (above) show differences by: * Type: false positive / false negative / incorrect level * Severity: color intensity reflects magnitude of error Plots (below) measure recovery order of groundtruth boundaries: * Ideally recover top-level objects, then parts, then subparts (levels 1, 2, 3) * gPb-UCM only recovers groundtruth hierarchy on simpler scenes (portraits) All Scenes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Overall Boundary Recall Level Recovery Fraction Boundary Recovery Order by Hierarchy Level Level 1 Level 2 Level 3 Portrait Scenes 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Overall Boundary Recall Level Recovery Fraction Boundary Recovery Order by Hierarchy Level Level 1 Level 2 Level 3 Dataset Parts Objects Image Complete labeling of 97 complex scenes photographed by artist Stephen Shore [4] Compared to Berkeley Segmentation Dataset [3]: more objects, greater scale range Interactive Labeling A B D C Browse object-part hierarchy by expanding/collapsing visible subtrees (A) Drag and drop to rearrange region hierarchy (B) Fade occlusion layers by depth to visualize figure/ground ordering (C) Interactively edit regions (D) Editor enforces parent-child region containment invariant Superpixel selection brush speeds region definition: Image Superpixels Click Drag Release Touch-up Scene Model Object-Part Occlusion Ordering man head shirt arm watch hand arm envelope stamp label glove Objects/Parts Figure/Ground Gnd Fig glove envelope stamp label man head V shirt arm V watch hand arm glove Image regions {R 1 ,R 2 , ..., R n } (R i , R j possibly overlapping) Map each region R i to a node in doubly-ordered tree: Parent-child node relationships correspond to object-part containment Relative ordering of sibling nodes resolves occlusion ambiguities Tree traversal: Recovers object-part hierarchy Converts local occlusion relationships into global figure/ground ordering Virtual nodes (dotted ovals): parts without visible boundaries Virtual links (dotted arrows): remap occlusion ordering (handle self-occlusion) Overview Annotate multiple modalities: Objects, parts, subparts Object-part containment Segmentation Occlusion (figure/ground) Unifying abstraction: region trees Web-based annotation tool: Computer-assisted segmentation Object segmentation dataset & benchmark Motivation: joint detection & segmentation [2] apple glasses P P P P P P P P q hat face torso eyes P P P P P P P P P q beard P P P P P P q finger shirt tie P P P P P P jacket Fig Gnd Image Objects/Parts Subparts Figure/Ground Ultrametric Contour Map Michael Maire 1 , Stella X. Yu 2 , and Pietro Perona 1 1 California Institute of Technology - Pasadena, CA 91125 2 University of California at Berkeley / ICSI - Berkeley, CA 94704 Hierarchical Scene Annotation

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Page 1: California Institute of Technology - Pasadena, CA 91125 ...mmaire/papers/pdf/seg_annotate_bmvc2013_poster.pdf1California Institute of Technology - Pasadena, CA 91125 2University of

[1] P. Arbelaez, M. Maire, C. Fowlkes, and J. Ma-lik. Contour Detection and Hierarchical ImageSegmentation. PAMI, 2011.

[2] M. Maire, S. X. Yu, and P. Perona. Object De-tection and Segmentation from Joint Embeddingof Parts and Pixels. ICCV, 2011.

[3] D. Martin, C. Fowlkes, D. Tal, and J. Malik. ADatabase of Human Segmented Natural Imagesand its Application to Evaluating SegmentationAlgorithms and Measuring Ecological Statistics.ICCV, 2001.

[4] M. Spain and P. Perona. Measuring and Predict-ing Object Importance. IJCV, 2010.

ONR MURI N00014-10-1-0933 and ARO/JPL-NASA Stennis NAS7.03001 supported this work. Part of Stella Yu’s work was supported by NSF CAREER IIS-1257700.

Benchmarks

Image Groundtruth UCM gPb-UCM [1] Residual

• Construct groundtruth Ultrametric Contour Map (UCM):– Weight each boundary by level at which it appears in object-part hierarchy– Determine boundary visibility by region tree traversal• Does machine hierarchical segmentation [1] respect object-part containment?

– Residuals (above) show differences by:∗ Type: false positive / false negative / incorrect level∗ Severity: color intensity reflects magnitude of error

– Plots (below) measure recovery order of groundtruth boundaries:∗ Ideally recover top-level objects, then parts, then subparts (levels 1, 2, 3)∗ gPb-UCM only recovers groundtruth hierarchy on simpler scenes (portraits)

All

Sce

nes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Overall Boundary Recall

Leve

l Rec

over

y F

ract

ion

Boundary Recovery Order by Hierarchy Level

Level 1Level 2Level 3

Port

rait

Sce

nes

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Overall Boundary Recall

Leve

l Rec

over

y F

ract

ion

Boundary Recovery Order by Hierarchy Level

Level 1Level 2Level 3

Dataset

Par

tsO

bjec

tsIm

age

• Complete labeling of 97 complex scenes photographed by artist Stephen Shore [4]• Compared to Berkeley Segmentation Dataset [3]: more objects, greater scale range

Interactive Labeling

A

BDC

• Browse object-part hierarchy by expanding/collapsing visible subtrees (A)• Drag and drop to rearrange region hierarchy (B)• Fade occlusion layers by depth to visualize figure/ground ordering (C)• Interactively edit regions (D)

– Editor enforces parent-child region containment invariant– Superpixel selection brush speeds region definition:

Image Superpixels Click Drag Release Touch-up

Scene ModelObject-Part ⇒

⇐OcclusionOrdering

man

head

shirt

arm

watch

hand

arm

envelope

stamp

label glove

Objects/Parts

Figure/G

round

Gnd

Fig

glove

envelope

stamp

label

man

head

V

shirt

arm

V

watch

hand

arm glove

• Image⇒ regions {R1, R2, ..., Rn} (Ri, Rj possibly overlapping)• Map each region Ri to a node in doubly-ordered tree:

– Parent-child node relationships correspond to object-part containment– Relative ordering of sibling nodes resolves occlusion ambiguities• Tree traversal:

– Recovers object-part hierarchy– Converts local occlusion relationships into global figure/ground ordering• Virtual nodes (dotted ovals): parts without visible boundaries• Virtual links (dotted arrows): remap occlusion ordering (handle self-occlusion)

Overview• Annotate multiple modalities:

– Objects, parts, subparts– Object-part containment– Segmentation– Occlusion (figure/ground)

• Unifying abstraction: region trees• Web-based annotation tool:

– Computer-assisted segmentation• Object segmentation dataset & benchmark• Motivation: joint detection & segmentation [2]

appleCCCCCCCCW

glassesPPPPPPPPq

hat�

face�

torso��

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eyes HHHHHj

PPPPPPPPPq

beardPPPPPPq

finger

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shirt��

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tiePPPP

PPi

jacket����������������

Fig

Gnd

Image Objects/Parts Subparts Figure/Ground Ultrametric Contour Map

Michael Maire1, Stella X. Yu2, and Pietro Perona11California Institute of Technology - Pasadena, CA 91125 2University of California at Berkeley / ICSI - Berkeley, CA 94704

Hierarchical Scene Annotation