Recognition by Probabilistic Hypothesis Construction

Recognition by Probabilistic Hypothesis Construction

P. Moreels, M. Maire, P. Perona

California Institute of Technology

Rich features,probabilistic,fast learning,

efficient matching

Background

• Huttenlocher & Ullman, 1990

Efficientmatching

Rich features

• Fischler & Elschlager, 1973• v.d. Malsburg et al. ‘93

• Burl et al. ‘96• Weber et al. ‘00• Fergus et al. ‘03

• Lowe ‘99, ‘04

Probabilistic constellations,

categories

Objective: Individual object recognition

• D.Lowe, constellation model.

• Hypothesis and score.

• Scheduling of matches.

• Experiments: compare with D.Lowe.

Outline

Lowe’s recognition system

Lowe’99,’04

…

Test image

Models

Constellation model

Burl’96, Weber’00, Fergus’03

• Principled

detection/recognition

• Learn parameters from data

• Model clutter, occlusion,

distortions

+

-

Lowe’s recognition system Constellation model

• High number of parameters (O(n2))• 5-7 parts per model

• many training examples needed

• learning expensive

• Many parts redundancy

• Learn from 1 image

• Fast

Pros and Cons

• Manual tuning of parameters

• Rigid planar objects

• Sensitive to clutter

How to adapt the constellation model to our needs ?

Reducing degrees of freedom

1. Common reference frame ([Lowe’99],[Huttenlocher’90])

2. Share parameters ([Schmid’97])

3. Use prior information learned on foreground and background ([FeiFei’03])

model m

position of model m

Foreground

Gaussian

shape pdf

Gaussian part

appearance pdf

Gaussian

relative scale pdf

log(scale)

Prob. of detection

0.8

Based on [Fergus’03][Burl’98]

Parameters and priors

0.8 0.75 0.9

Gaussian background

appearance pdf

ClutterConstellation model

Gaussian part

appearance pdfGaussian

relative scale pdf

log(scale)

Prob. of detection

0.80.2 0.2 0.2

Gaussian background

appearance pdf

Gaussian conditional

shape pdf

Foreground ClutterSharing parameters

Hypotheses – features assignments

= models from database

New scene (test image)

. . .

Interpretation

. . .

Models fromdatabaseNew scene (test image)

Hypotheses – model position

1

2

3

Θ = affine transformation

Score of a hypothesis

Hypothesis:model + position + assignments

observed featuresgeometry + appearance

database of models

(Bayes rule)

constantConsistency Hypothesis probability

Score of a hypothesis

- Consistency between observations and hypothesis

- Probability of number of clutter detections

- Probability of detecting the indicated model features

- Prior on the pose of the given model

foreground features ‘null’ assignments

geometry geometryappearance appearance

Efficient matching process

Scheduling – inspired from A*

empty hypothesis

1 assignment …

scene features, no assignment done

PP P Pperfect completion

(admissible heuristic, used as a guide for the search)

Increase computational efficiency:

- at each node, searches only a fixed number of sub-branches

- forces terminationPearl’84,Grimson’87

‘null’ assignment

… …2 assignments

P P P P

P Pcan be compared

explore most promising branches first

P P

Score

….models

fromdatabase

New scene

Recognition: the first match No clue regarding geometry first match based on appearance

best match

second best match

features Initialization ofhypotheses queue

….

P P P P P

P P P P P

P P P P P

….models

fromdatabase

New scene

Scheduling – promising branches first

features Updated hypothesesqueue

….

P P P P

P P P

P P P?

Experiments

Toys database – models

153 model images

Toys database – test images (scenes)

- 90 test images- multiple objects or different view of model

100 model images

Kitchen database – models

Kitchen database – test images

- 80 test images- 0-9 models / test image

Test image Identified modelTest image Identified model

Examples

Lowe’s model implemented using [Lowe’97,’99,’01,’03]

Lo

we’

s m

eth

od

Ou

r sy

stem

a. Object found, correct pose Detection

b. Object found, incorrect pose False alarm

c. Wrong object found False alarm

d. Object not found Non detection

Performance evaluation

Test image hand-labeledbefore the experiments

Results – Toys imagesS

cene

s (t

est

imag

es)

Mod

els

(dat

abas

e)

- 80% recognition with false alarms / test set = 0.2- Lower false alarm rate than Lowe’s system.

- 153 model images- 90 test images- 0-5 models / test image

Results – Kitchen images

- Achieves 77% recognition rate with 0 false alarms

- 100 training images- 80 test images- 0-9 models / test image- 254 objects to be detected

• Unified treatment• Best of both worlds

• Probabilistic interpretation of Lowe [‘99,’04].• Extension of [Burl,Weber,Fergus ‘96-’03] to many-features,

many-models, one-shot learning.

• Higher performance• Comparison with Lowe [‘99,’04].

• Future work: categories

Conclusions