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r 1. r 4. r 5. r 2. r 3. r 6. Efficiently Selecting Regions for Scene Understanding. S T A N F O R D. Aim: To efficiently select accurate, discriminative regions for a high-level vision task. Results. Integer Program. Semantic Segmentation.

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r1

r4

r5

r2

r3

r6

Efficiently Selecting Regions for Scene Understanding

S

T

A

N

F

O

R

D

Aim:To efficiently select accurate, discriminative regions for a high-level vision task

Results

Integer Program

Semantic Segmentation

Integer constraints yr(i)  {0,1}

Background Dataset - 715 images

S - set of super-pixels (intersection of segments)

80/20 train/test split, 4 folds

Over-segmentations as Regions

Minimize Energy

Simple Inference

r,r’  D

minyr,i r(i)yr(i) + (r,r’),i,j rr’(i,j)yrr’(i,j)

Baselines

  • Pixel-based Model

Uniqueness

i yr(i) = 1

i  L’ = {0} (not selected)  L

SEGMENTS

SEGMENTS

RESULT

RESULT

  • Intersection of over-segmentations

j  L’, (r,r’) - neighbors

Marginalization

j yrr’(i,j) = yr(i)

  • Lowest energy over-segmentation

r “covers” s iL yr(i) = 1

Each super-pixel is covered

Covering

  • Gould et al., 2009

I

M

A

G

E

Linear Programming Relaxation

Relax yr(i)  [0,1]

SCENE LAYOUT, Hoiem et al, 2005

MONOCULAR 3D, Saxena et al, 2008

Standard pairwise energy. Dual decomposition with tree slaves.

  • Segments are not accurate

  • Do not align with scene boundaries

SEGMENTS

RESULT

Sub-dictionary DT D that forms a tree. Union of DT = D.

P

I

X

E

L

Minimize energy such that

uniqueness, marginalization and

integer constraints are satisfied.

  • Segments are not discriminative

  • Too small to capture useful cues

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r5

Belief

propagation

r2

r3

r4

SEGMENTATION, Gould et al, 2008

I

N

T

E

R

Overview

Region Selection using Energy Minimization

Tree slaves do not enforce the covering constraint.

Large number of possible pixel-to-region assignments

Sub-dictionary DC D that covers s. One slave for each s  S.

S

E

G

M

Exact inference is intractable

Move-making algorithm

s

Minimize energy such that

exactly one region in DC

is selected.

Linear Search

Over Regions

DICTIONARY

OF

REGIONS

D

MERGE AND INTERSECT

WITH SEGMENTS TO FORM

PUTATIVE REGIONS

r1

r3

r2

G

O

U

L

D

Standard linear programming relaxation is not tight.

Clique constraints

Choose a subset of 3 super-pixels SQ  S.

CURRENT REGIONS

SEGMENTATIONS

Sub-dictionary DQ D, each region covers at least one s  SQ

SELECT REGIONS

ITERATE UNTIL

CONVERGENCE

O

U

R

Minimize energy such that

uniqueness, marginalization,

covering and integer constraints are satisfied.

Efficient

Enumeration

Select regions from D (with overlapping regions) such that

  • No regions overlap

  • Entire image is covered

Integer

Program

  • Energy of the vision task is minimized

Shrinking Strategy

Maintain a Small Active Set of Regions

De-activate regions not selected in any slave for T iterations

Two dictionary moves

  • Merge neighboring regions

  • Merge and intersect regions with segments

Iteratively introduce cliques with maximum active regions

Statistically significant improvement


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