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Object Segmentation. Jaechul Kim and Kristen . Grauman. University of Texas at Austin. Problem statement. Category-independent object segmentation: . Generate object segments in the image . regardless of their categories.. ID: 142582

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Slide1

Shape Sharing for Object Segmentation

Jaechul Kim and Kristen GraumanUniversity of Texas at Austin

Slide2

Problem statement

Category-independent object segmentation: Generate object segments in the image regardless of their categories.

Slide3

Related work

Top-down, category-specific approache.g., Active Contours (IJCV 1987), Borenstein and Ullman (ECCV 2002), Levin and Weiss (ECCV 2006), Kumar et.al. (CVPR 2005)

Use generic knowledge on object shapese.g., Levinshtein et.al. (ICCV 2009)

Category-independent multiple segmentationse.g., Malisiewicz and Efros (BMVC 2007), Carreira and Sminchisescu (CVPR 2010), Endres and Hoiem (ECCV 2010)

Learn local shapese.g., Ren et.al. (ECCV 2006), Opelt et.al. (CVPR 2006)

Slide4

Spectrum of existing approaches

Class-specific

Bottom-up

+ coherent mid-level regions

+ applicable to any image

- prone to over/under-segment

+ robustness to low-level cues- typically viewpoint specific- requires class knowledge!

How to model

top-down shape in a category-independentway?

horse shape priors

color, textures, edges…

Malisiewicz

and

Efros

(BMVC 2007),

Arbelaez et al. (CVPR 2009)Carreira and Sminchisescu (CVPR 2010)Endres and Hoiem (ECCV 2010)

Active Contours (IJCV 1987)Borenstein and Ullman (ECCV 2002) Levin and Weiss (ECCV 2006)Kumar et.al. (CVPR 2005)

e.g.,

e.g.,

Slide5

Our goal

Segment even unfamiliar objects with category-independent top-down cues

Top-down segmentation with shape prior

We don’t want to care what is in the image.

Cow? Sheep?

Slide6

Our idea:Shape sharing

Semantically close

Semantically disparate

Object shapes are shared among different categories.

Shapes from one class can be used to segment another (

possibly unknown

) class:

Enable category-independent shape priors

Slide7

Basis of approach:

transfer through matching

Transfer category-independent shape prior

Exemplar image

Test image

Partial shape

match

Global shape

projection

ground truth

object boundaries

Slide8

1. Shape projection

via local shape matches

Exemplars

Test image

Approach: Overview

+

Shape prior

Color model

Segmentation model per each group

2. Aggregating

the shape projections

Graph-cut

Segmentation hypotheses

3. Multiple figure-ground segmentations

with shape prior

Slide9

Approach: Shape projection

Test image

Exemplars

Projection

Aggregation

Segmentation

Vs.

BPLRs

Superpixels

Boundary-Preserving Local Regions (BPLR):

Distinctively shaped

Dense

Repeatable

[Kim & Grauman, CVPR 2011]

Slide10

Approach: Shape projection

Test image

Exemplars

Shape projections via

similarity transform of BPLR matches

Projection

Aggregation

Segmentation

Matched

Exemplar 1

Matched

Exemplar 2

Shape hypotheses

Slide11

Approach: Refinement of projections

Refined shape

Initial projection

Exemplar

jigsaw

Projection

Aggregation

Segmentation

Include

superpixels

where majority of pixels overlap projection

Align with bottom-up evidence

Slide12

Approach: Aggregating projections

Projection

Aggregation

Segmentation

Exploit

partial

agreement

from multiple exemplars

Grouping based on overlap

Slide13

+

Shape prior

Color model

Segmentation model per

each

group

Approach: Segmentation

Projection

Aggregation

Segmentation

Figure-ground segmentation using graph-cut

Slide14

n-links

Projection

Aggregation

Segmentation

Approach: Graph-cut

data term

smoothness term

Define a graph over image pixels:

node = pixel

edge = cost of a cut between pixels

Energy function to minimize:

Slide15

Projection

Aggregation

Segmentation

Approach:

Segmentation

Color likelihood

Fg

Bg

NA

Fg

color histogram

Bg

color histogram

Shape likelihood

Graph-cut optimization

Data term

+

Smoothness term

Slide16

Projection

Aggregation

Segmentation

Approach: Multiple segmentations

Parameter controlling

data term bias

Compute multiple segmentations

by varying

foreground bias:

Output:

Carreira

and

Sminchisescu

,

CPMC: Automatic Object Segmentation Using Constrained Parametric Min-Cuts

PAMI

2012

.

Slide17

Experiments

Exemplar database: PASCAL 2010 segmentation task training set (20 classes, 2075 objects)

Test datasets: PASCAL 2010 segmentation task validation set (20 classes, 964 images) Berkeley segmentation dataset (natural scenes and objects, 300 images)

Baselines: CPMC [Carreira and Sminchisescu, PAMI 2012] Object proposals [Endres and Hoiem, ECCV 2012] gPb+owt+ucm [Arbelaez et al., PAMI 2011]

Evaluation metric:Best covering score w.r.t # of segments

Ground truth

0.92

0.75

0.71

Best covering score: 0.92

Slide18

Segmentation quality

ApproachCovering (%)Num of segmentsShape sharing (Ours)84.31448CPMC [Carreira and Sminchisescu]81.61759Object proposals [Endres and Hoiem]81.71540gPb-owt-ucm [Arbelaez et al.]62.81242

PASCAL 2010 dataset

ApproachCovering (%)Num of segmentsShape sharing (Ours)75.61449CPMC [Carreira and Sminchisescu]74.11677Object proposals [Endres and Hoiem]72.31275gPb-owt-ucm [Arbelaez et al.]61.61483

Berkeley segmentation dataset

*Exemplars = PASCAL

Slide19

When does shape sharing help most?

Gain as a function of color easiness and object size

Easy to segment by color

Hard to segment by color

Compared to CPMC [

Carreira

and Sminchisescu., PAMI 2012]

Slide20

Which classes share shapes?

Animals

Vehicles

Semantically disparate

Unexpected

pose variations

Slide21

Shape sharing (ours)

CPMC (Carreira and Sminchisescu)

0.889

0.859

0.903

0.935

0.599

0.638

0.630

0.694

Objects with diverse colors

Example results (

good

)

Slide22

Shape sharing (ours)

CPMC (Carreira and Sminchisescu)

Objects confused by surrounding colors

Example results (good)

0.966

0.875

0.999

0.928

0.508

0.533

0.526

0.685

Slide23

Shape sharing (ours)

CPMC (Carreira and Sminchisescu)

Example results (failure cases)

0.220

0.199

0.713

0.406

0.818

0.934

0.973

0.799

Slide24

Shape sharing: highlights

Non-parametric transfer of shapes across categoriesPartial shape agreement from multiple exemplarsMultiple hypothesis approachMost impact for heterogeneous objects

Code

is available:

http://vision.cs.utexas.edu/projects/

shapesharing

Top-down shape prior in a category-independent way

Slide25

Approach: Refinement and pruning

Initial projection

Refined shape

Exemplar

jigsaw

Pruned out

Exemplar

Projection

Aggregation

Segmentation

Slide26

Segmentation quality

Quality of initial shape projections

Initial shape prior

Exemplar

Approach

Covering (%)

Num

of

segmentsExemplar-based merge (Ours)77.0607Neighbor merge [1]72.25005Bottom-up segmentation [2]62.81242

[

1]

Malisiewicz

and

efros

, BMVC 2007.

[2]

Arbelaez

et.al., PAMI 2011.

Slide27

Impact of shapes

CPMC, PASCAL

CPMC, BSD

Object proposal, PASCAL

Object proposal, BSD

Shape sharing’s gain in recall as a function of overlap

Slide28

Category-independent vs. dependent

ApproachCovering (%)Category-specific84.7Category-independent (default)84.3Strictly category-independent83.9CPMC81.6Object proposals81.7

Comparison of category-independent shape prior and category-specific variants


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