EpitomeEpitome

Ji Soo Yi and Woo Young Kim

Instructor: Prof. James Rehg

April 27, 2004.

Spring 2004, CS7636 Computational Perception

CONTENTS

Introduction

Epitomic Image

Experiment

Results & Conclusion

Future direction

Edited by Woo Young and Ji Soo

Introduction(1)

Image representative modelFeature-based

Geometric approach

Template-based Standard Euclidian error norms Eigen spaces

Color histogram-based

Edited by Woo Young and Ji Soo

Introduction(2)

Epitomic image analysisWhat is Epitome?

The miniature, condensed version of image. Still consists of most constitutive elements. Use a probabilistic measure of similarities. Shape epitome and appearance epitome.

Edited by Woo Young and Ji Soo

Introduction(3)

Epitomic image analysisGraphical model of epitomic analysis

Edited by Woo Young and Ji Soo

Es

MS1S2

I

Emappearance

epitome

shape

epitome

I=M*S1+(1-M)*S2 + noise

Introduction(4) Epitomic image analysis

Probabilistic framework

Edited by Woo Young and Ji Soo

epitome

e = (,)

M,N

Patch Zk = {zi,k}, zi,k= xi

Input image X

Patch Zn

Me, Ne

Tk

Tn

Introduction(5) Epitomic image analysis

EM algorithm to extract an epitomic image

Edited by Woo Young and Ji Soo

E step:

M step:

Epitomic Image (1)

Edited by Woo Young and Ji Soo

Original image Epitomic image

Epitomic Image (2)

Edited by Woo Young and Ji Soo

Input imageEpitomic image

Experiment (1)

Edited by Woo Young and Ji Soo

Epitomic Modeling

Face Detection

Comparison with PCA Analysis

Experiment (2)

Edited by Woo Young and Ji Soo

Epitomic Modeling

Training data – a set of face images

Each image : 100 by 75Epitomic image: 32 by 32

Epitomic image

Experiment (3)

Edited by Woo Young and Ji Soo

Epitomic Modeling

Training data – a synthetic image by tiling face images

100 by 75 pixels for each image

1000 by 375 pixels for total

75 by 75 pixels

Experiment (4)

Edited by Woo Young and Ji Soo

Face Detection

Histogram and clustering

Experiment(5)

Edited by Woo Young and Ji Soo

Face Detection

Patch matching – face image

High log likelihood – good match Low log likelihood - poor match

Experiment(6)

Edited by Woo Young and Ji Soo

Face Detection

Patch matching – non face image

Low log likelihood – good match High log likelihood - poor match

Experiment(7)

Detection rate of PCA analysis

0.92

0.660.620

0.00

training face testing face

dete

ctio

n ra

te

Rigid

Non-Rigid

Edited by Woo Young and Ji Soo

Comparison with PCA analysis – PCA

Rigid data

Non-Rigid data

Experiment(8)

Detection rate of Epitomic analysis

0.700 0.7000.725 0.725

face nonface

Dete

ctio

n ra

te

Rigid

Non-Rigid

Edited by Woo Young and Ji Soo

Comparison with PCA analysis – Epitome

Rigid data

Non-Rigid data

Results & Conclusion

Edited by Woo Young and Ji Soo

Epitomic image modeling

Parameter settings

Comparison with PCA Analysis

Statistics

Future direction

Edited by Woo Young and Ji Soo

Computational time saving

Shape epitome

Other applications