Epitome Ji Soo Yi and Woo Young Kim Instructor: Prof. James Rehg April 27, 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 Edited by Woo Young and Ji Soo

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. Edited by Woo Young and Ji Soo

Introduction(3)  Epitomic image analysis Graphical model of epitomic analysis Edited by Woo Young and Ji Soo EsEs M S1S1 S2S2 I EmEm appearance epitome shape epitome I=M*S 1 +(1-M)*S 2 + noise

Introduction(4)  Epitomic image analysis Probabilistic framework Edited by Woo Young and Ji Soo epitome e = ( ,  ) M,N Patch Z k = {z i,k }, z i,k = x i Input image X Patch Z n Me, Ne TkTk TnTn

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 image Epitomic 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 75 Epitomic 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 matchLow log likelihood - poor match

Experiment(6) Edited by Woo Young and Ji Soo Face Detection Patch matching – non face image Low log likelihood – good matchHigh log likelihood - poor match

Experiment(7) Edited by Woo Young and Ji Soo Comparison with PCA analysis – PCA Rigid data Non-Rigid data

Experiment(8) 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