S. Savarese, 2003

P. Buegel, 1562

Constellation model of Constellation model of object categoriesobject categories

Fischler & Elschlager 1973 Yuille ‘91Brunelli & Poggio ’93 Lades, v.d. Malsburg et al. ‘93Cootes, Lanitis, Taylor et al. ’95 Amit & Geman ‘95, ’99Perona et al. ‘95, ‘96, ’98, ’00, ’03 Many more recent works…

X (location)

(x,y) coords. of region center

A (appearance)

Projection ontoPCA basis

c1

c2

c10

…..

normalize

11x11 patch

X (location)

(x,y) coords. of region center

A (appearance)

Projection ontoPCA basis

c1

c2

c10

…..

normalize

11x11 patchX A

h

XX

I

AA

The Generative Model

Hypothesis (h) node

X A

h

XX

I

AA

h is a mapping from interest regions to parts

3

5

91

2

46

7 10

8

e.g. hi = [3, 5, 9]

X A

h

XX

I

AA

h is a mapping from interest regions to parts

3

5

91

2

46

7 10

8

e.g. hj = [2, 4, 8]

The hypothesis (h) node

(x1,y1)

(x2,y2)

(x3,y3)X A

h

XX

I

AA

The spatial node

Spatial parameters node

X A

h

I

AA

Joint Gaussian

Joint density over all parts

XX

The appearance node

PCA coefficients on fixed basis

Pt 1. (c1, c2, c3,…)

Pt 2. (c1, c2, c3,…)

Pt 3. (c1, c2, c3,…)

X A

h

I

AAXX

X A

h

I

Appearance parameter node

Gaussian

Independence assumed between the P parts

P

Fixed PCA basis

XX AA

X A

h

I

P

Maximum Likelihood interpretation

observed variables

hidden variable

parametersXX AA

Also have background model – constant for given image

X A

h

I

PXX AA

a0X

B0X

m0X

β0X

a0A

B0A

m0A

β0A

MAP solutionChoose conjugate form:

Normal – Wishart distributions:

P(, ) = p(|)p() = N(|m, β ) W(|a,B)

observed variables

hidden variable

parameters

Introduce priors over parameters

priors

X A

h

I

PXX AA

a0X

B0X

m0X

β0X

a0A

B0A

m0A

β0A

Variational Bayesian model

observed variables

hidden variable

parameters

Estimate posterior distribution on parameters – approximate with Normal – Wishart-- has parameters: {mX, βX, aX, BX, m A, βA, aA, BA}

priors

ML/MAP LearningML/MAP Learning

n

1

2

where = {µX, X, µA, A}

ML/MAP

Weber et al. ’98 ’00, Fergus et al. ’03

X A

h

I

PAAXX

Performed by EM

Bayesian

Variational LearningVariational Learning

n

1

2

Parameters to estimate: {mX, βX, aX, BX, mA, βA, aA, BA}i.e. parameters of Normal-Wishart distributionFei-Fei et al. ’03, ‘04

X A

h

I

PXX AA

a0X

B0X

m0X

β0X

a0A

B0A

m0A

β0A

Performed by Variational Bayesian EM

E-Step

Random initializationVariational EMVariational EM

prior knowledge of p()

new estimate of p(|train)

M-Step

new ’s

(Attias, Hinton, Beal, etc.)

No labeling No segmentation

No alignment

Weakly supervised learningWeakly supervised learning

ExperimentsExperimentsTraining:

1- 6 images

(randomly drawn)

Detection test:

Datasets: foreground and background

The Caltech-101 Object Categories

www.vision.caltech.edu/feifeili/Datasets.htm

50 fg/ 50 bg images

object present/absent

The prior

• Captures commonality between different classes

• Crucial when training from few images

• Constructed by:– Learning lots of ML models from other classes– Each model is a point in θ space– Fit Norm-Wishart distribution to these points using moment

matching i.e. estimate {m0X, β0

X, a0X, B0

X, m0A, β0

A, a0A, B0

A}

What priors tell us? – 1. meansAppearanc

e

likely unlikely

Shape

What priors tell us? – 2. variability

Renoir

Picasso, 1951

Picasso, 1936

Miro, 1949

Warhol, 1967

Magritte, 1928

Arcimboldo, 1590

Da Vinci, 1507

likely

un

likely

Appearance Shape

The prior on AppearanceBlue: Airplane; Green: Leopards; Red: Faces Magenta: Background

The prior on Shape

X-coord Y-coord

Blue: Airplane; Green: Leopards; Red: Faces Magenta: Background

Motorbikes• 6 training images• Classification task (Object present/absent)

Grand piano

Cougar faces

Number of classes in prior

How good is the prior alone?

Performance over all 101 classes

Conclusions

• Hierarchical Bayesian parts and structure model

• Learning and recognition of new classes assisted by transferring information from unrelated object classes

• Variational Bayes superior to MAP

Visualization of learning

Sensitivity to quality of feature detector

Discriminative evaluation

Mean on diagonal:18%

More recent workby Holub, Welling & Perona40%Using gen./disc hybrid