EpitomeEpitomeJi 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 model

Feature-based Geometric approach

Template-based Standard Euclidian error norms Eigen spaces

Color histogram-based

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Introduction(2)Epitomic image analysis

What 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.

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Introduction(3)Epitomic image analysis

Graphical model of epitomic analysis

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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

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E step:

M step:

Epitomic Image (1)

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Original image Epitomic image

Epitomic Image (2)

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Input imageEpitomic image

Experiment (1)

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Epitomic Modeling

Face Detection

Comparison with PCA Analysis

Experiment (2)

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Epitomic Modeling

Training data – a set of face imagesEach image : 100 by 75Epitomic image: 32 by 32

Epitomic image

Experiment (3)

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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)

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Face Detection

Histogram and clustering

Experiment(5)

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Face Detection

Patch matching – face image

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

Experiment(6)

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Face Detection

Patch matching – non face image

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

Experiment(7)

Detection rate of PCA analysis0.92

0.660.620

0.00

training face testing face

detection rate Rigid

Non-Rigid

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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

ction

rate

Rigid Non-Rigid

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Comparison with PCA analysis – Epitome

Rigid data

Non-Rigid data

Results & Conclusion

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Epitomic image modeling

Parameter settings

Comparison with PCA Analysis

Statistics

Future direction

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Computational time saving

Shape epitome

Other applications