Essence The of the Scene Gist Trayambaka Karra KT and Garold Fuks

Essence The of the Scene Gist

Trayambaka KarraKTand

Garold Fuks

The “Gist” of a scene

If this is a street this must be a pedestrian

Physiological Evidence

• People are excellent in identifying pictures (Standing L., QL. Exp. Psychol. 1973)

Gist: abstract meaning of scene • Obtained within 150 ms (Biederman, 1981, Thorpe S. et.al 1996 )

• Obtained without attention (Oliva & Schyns, 1997, Wolfe,J.M. 1998)

• Possibly derived via statistics of low-level structures) e.g. Swain & Ballard, 1991(

• Change Blindness (seconds)(Simons DJ,Levin DT,Trends Cog.Sci. 97)

What is the “gist”

• Inventory of the objects

(2-3 objects in 150 msec Luck & Vogel, Nature 390, 1997 )

• Relation between objects (layout) (J. Wolfe, Curr. Bio. 1998, 8 )

• Presence of other objects

• “Visual stuff” – impression of low level

features

How does the “Gist” works

Statistical Properties

Object Properties

R.A. Rensink, lecture notes

Outline• Context Modeling

– Previous Models– Scene based Context Model

• Context Based Applications– Place Identification– Object Priming– Control of Focus of Attention– Scale Selection– Scene Classification

• Joint Local and Global Features Applications– Object Detection and Localization

• Summary

Probabilistic Framework

)()(

)/()/( OP

vP

OvPvOP

MAP Estimator

v – image measurementsO – object property

Category (o)Location (x)Scale (σ)

Object-Centered Object Detection

B. Moghaddam, A. Petland IEEE, PAMI-19 1997

)()(

)/()/()/( OP

vP

OvPvOPvOP

L

LL

• The only image features relevant to object detection are those belonging to the object and not the background

The “Gist” of a scene

Local features can be ambiguous Context can provide prior

Scene Based Context Model

)/()/(

),/(),/()/( C

CL

CLCL vOP

vvP

vOvPvvOPvOP

Background provides a likelihood of finding an object

Prob(Car/image) = lowProb(Person/image) = high

Context Modeling

Previous Context Models(Fu, Hammond and Swain, 1994,Haralick, 1983; Song et al, 2000)

Rule Based Context Model Object Based Context Model

Scene centered context representation(Oliva and Torralba, 2001,2002)

Structural Description

O2

O2

O4

O3

O1

O4

Above

Above

Right-of Left-of

Touch

Rule Based Context Model

Rule Based Context Model

Fu, Hammond and Swain, 1994

Object Based Context Model

),...,()/(),...,,,...,( 11

11 N

N

iiiNN OOPOvPvvOOP

• Context is incorporated only through prior probability of object combinations in the world

R. Harralick, IEEE, PAMI-5 1983

Scene Based Context Model

What are the features representing scene - ?

• Statistics of local low level features• Color histograms• Oriented band pass filters

Context Features - Vc

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)'()'(),(x

k xxgxIkxv

g1(x)

g2(x)

gK(x)

v(x,1)

v(x,2)

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Car , no peoplePeople, no car

Gabor filter

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),(1 kx ),(2 kx

PCA

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Context Features - Summary

x k

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Kkkxv 1,( Bank Of

Filters

DimensionReduction

PCA

I(x)

DnnC avfeaturesContext ,...,1

Probability from Features

)/()/(

),/(),/()/( C

CL

CLCL vOP

vvP

vOvPvvOPvOP

How to obtain context based probability priors P(O/vc) on object properties - ?

• GMM - Gaussian Mixture Model • Logistic regression• Parzen window

Probability from Features GMM

)()/()()/()(

)()(

)/()/(

OPOvPOPOvPvP

OPvP

OvPvOP

CCC

C

CC

M

iiiCiC vGwOvP

1

,;/

P(Object Property/Context)

Need to study two probabilities: P(vc/O) – likelihood of the features given the presence of an objectP(vc/¬O) – likelihood of the features given the absence of an object

Gaussian Mixture Model:

The unknown parameters are learnt by EM algorithm

Miiiiw 1,,




Probability from Features Logistic Regression

D

iCiC ivaavF

10 )()(

)()/(

)/(log C

C

C vFvOP

vOPLogit

)(1

1/

CvFC evOp

Probability from Features Logistic Regression

0a

20)/(

)/(log 10

ageaavOP

vOP

C

C

1a

O = having back problemsvc = age

- The log odds for 20 year old person

- The log odds ratio when comparing two persons who differ 1 year in age

Example

Training Stage

Working Stage 20101

1/

ageaaeageOp




Probability from Features Parzen Window

j

jCC vvKOvP /

2

2

v

ekvK

Radial Gaussian Kernel

What did we have so far…

• Context Modeling

• Context Based Applications– Place Identification– Object Priming– Control of Focus of Attention– Scale Selection– Scene Classification

Place IdentificationGoal: Recognize specific locations

jjCjC

jjjC

jjCCj

vvKPlacevP

PlacePPlacevP

PlacePPlacevPvPlaceP

/

/

//

Place Identification

A.Torralba, K.Murphy, W. Freeman, M. Rubin ICCV 2003

Place Identification Cj

jvPlaceP /maxDecide only when

Precision vs. Recall rate:

A.Torralba, P. Sinha, MIT AIM 2001-015

Object Priming• How do we detect objects in an image?

– Search the whole image for the object model.

– What if I am searching in images where the object doesn’t exist at all?

• Obviously, wasting “my precious” computational resources. --------- GOLUM.

• Can we do better and if so, how?

– Use the “great eye”, the contextual features of the image (vC), to predict the probability of finding our object of interest, o in the image i.e. P(o / vC).

Object Priming …..

• What to do?– Use my experience to learn

from a database of images with

• How to do it?– Learn the PDF , by a mixture of

Gaussians– Also, learn the PDF

M

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1

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)()/()()/()( oPovPoPovPvP CCC

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1

),;(/

Object Priming..…

Object Priming..…

Control of Focus of Attention

• How do biological visual systems use to deal with the analysis of complex real-world scenes?

– by focusing attention into image regions that require detailed analysis.

Modeling the Control of Focus of Attention

How to decide which regions are “more” important than others?

• Local–type methods1. Low level saliency maps – regions that have different

properties than their neighborhood are considered salient.

2. Object centered methods.

• Global-type methods1. Contextual control of focus of attention

Contextual Control of Focus of Attention

• Contextual control is both

– Task driven (looking for a particular object o) and

– Context driven (given global context information: vC)

• No use of object models (i.e. ignores object centered features)

Contextual Control of Focus of Attention …

Contextual Control of Focus of Attention…

• Focus on spatial regions that have high probability of containing the target object o given context information (vC)

• For each location x, lets calculate the probability of presence of the object o given the context vC.

• Evaluate the PDF based on the past experience of the system.

ContextObjectLocationP ,/


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iiiCi

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voxPeiContextObjectLocationP

1

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,,,

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Learning Stage: Use the Swiss Army Knife, the EM algorithm, to estimate the parameters


Scale Selection• Scale selection is

• a fundamental problem in computer vision.

• a key bottleneck for object-centered object detection algorithms.

• Can we estimate scale in a pre-processing stage?

• Yes, using saliency measures of low-level operators across spatial scales.

• Other methods? Of course, …..

Context-Driven Scale Selection

?,/..

,/,,/

CvoPei

ContextObjectScalePContextObjectLocationScaleP

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iiiCi

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Preferred Scale,

Context-Driven Scale Selection.…

Context-Driven Scale Selection.…

Scene Classification

sCClassifier

CClassifierC vsSceneP

vsScenePvsSceneP

/

//

• Strong correlation between the presence of many types of objects.

• Do not model this correlation directly. Rather, use a “common” cause, which we shall call “scene”.

• Train a Classifier to identify scenes.

•Then all we need is to calculate

What did we have so far…

• Context Modeling• Context Based Applications• Joint Local and Global Features Applications

– Object Detection and Localization

Need new tools: Learning and Boosting

Weak Learners• Given (x1,y1),…,(xm,ym) where

• Can we extract “rules of thumb” for classification purposes?

• Weak learner finds a weak hypothesis (rule of thumb)

h : X {spam, non-spam}

},{

}{

spamnonspamYy

emailsofsetXx

i

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Decision Stumps

• Consider the following simple family of component classifiers generating ±1 labels:

h(x;p) = a[xk > t] - b

where p = {a, b, k, t}. These are called decision stumps.

• Sign (h) for classification and mag (h) for a confidence measure.

• Each decision stump pays attention to only a single component of the input vector.

Ponders his maker, ponders his will

• Can we combine weak classifiers to produce a single strong classifier in a simple manner:

hm(x) = h(x;p1) + …. + h(x;pm)

where the predicted label for x is the sign of hm(x).

• Is it beneficial to allow some of the weak classifiers to have more “votes” than others:

hm(x) = α1h(x;p1) + …. + αmh(x;pm)

where the non-negative votes αi can be used to emphasize the components more reliable than others.

Boosting

What is boosting?– A general method for improving the accuracy of any

given weak learning algorithm.

– Introduced in the framework of PAC learning model.

– But, works with any weak learner (in our case the decision stumps).

Boosting …..

• A boosting algorithm sequentially estimates and combines classifiers by re-weighting training examples (each time concentrating on the harder examples)– each component classifier is presented with a slightly

different problem depending on the weights

• Base Algorithms– a set of “weak” binary (±1) classifiers h(x;p) such as

decision stumps

– normalized weights D1(i) on the training examples, initially set to uniform (D1(i) = 1 / m)

AdaBoost

1. At the tth iteration we find a weak classifier h(x;pt) for which the classification error is better than chance.

2. The new component classifier is assigned “votes” based on its performance

3. The weights on the training examples are updated according to

where Zt is a normalization factor.

));()((5.05.01

m

itiitt pxhyiD

)/)1log((5.0 ttt

t

itittt Z

xhyiDiD

))(exp()()(1

AdaBoost

Gambling

Uri

Gari KT

Object Detection and Localization

• 3 Families of Approaches– Parts based

• Object defined as spatial arrangement of small parts.

– Region based • Use segmentation to extract a region of image from the background

and deduce shape and texture info from its local features.

– Patch based• Use local features to classify each rectangular image region as

object or background.

• Object detection is reduced to a binary classification problem i.e compute just P(OC

i = 1 / vCi)

where OCi = 1 if patch i contains (part of) an object of class C

vCi = the feature vector for patch i

computed for class C.

Feature Vector for a Patch: Step 1



Summary: Feature Vector Extraction

12 * 30* 2 = 720 features

Filters and Spatial Templates

Object Detection …..

• Do I need all the features for a given object class?

• If so, what features should I extract for a given object class?

– Use training to learn which features are more important than others.

Classifier: Boosted Features• What is available?

– Training data is v = the features of the patch containing an object o.

• Weak learners pay attention to single features:

– ht(v) picks best feature and threshold:

• Output is

– ht(v) = output of weak classifier at round t– αt = weight assigned by boosting

• ~100 rounds of boosting

t

tt vhv )()(

)()( kvvht

Examples of Learned Features

Example Detections

Using the Gist for Object Localization

• Use gist to predict the possible location of the object.

• Should I run my detectors only in that region?– No! Misses detection if the object is at any other location.

– So, search everywhere but penalize those that are far from predicted locations.

• But how?

Using the Gist for Object Localization.…

• Construct a feature vector

which combines the output of the boosted classifier, and the difference .

• Train another classifier to compute

CCi

Ci xxvf

,,

CCi xx

Civ

.

.

classthisofobjectsforlocationpredictedx

patchtheoflocationxC

Ci

CCi

Ci

Ci xxvfOP

,,/1

Using the Gist for Object Localization.…

Summary

• Context Modeling– Previous Models– Scene based Context Model

Summary

• Context Modeling• Context Based Applications

– Place Identification– Object Priming– Control of Focus of Attention– Scale Selection– Scene Classification

Summary

• Context Modeling• Context Based Applications• Joint Local and Global Features Applications

– Object Detection and Localization

Documents

Essence The of the Scene Gist Trayambaka Karra KT and Garold Fuks