An

Ada

ptive

Skin M

odel

An

Ada

ptive

Skin M

odel

and

Its

App

licat

ion

to

and

Its

App

licat

ion

to

Obj

ection

able I

mag

e Fi

lter

ing

Obj

ection

able I

mag

e Fi

lter

ing

Qia

ngZh

uC.

T. W

u K

.T. C

heng

Y.

L. W

u*

Elec

tric

al &

Com

pute

r En

gine

erin

g U

nive

rsit

y of

Cal

ifor

nia,

San

ta B

arba

ra

* VI

MA

Tec

hnol

ogy

Chal

leng

e fo

r Sk

in D

etec

tion

151

mill

ion

skin

pix

els

448

mill

ion

non-

skin

pix

els

Hue

Satu

rati

on

Probability

ProbabilityN

on-t

rivi

al o

verl

ap b

etwe

en t

wo d

istr

ibut

ions

98%

of

colo

r bi

ns, c

onta

inin

g sk

in p

ixel

s, a

lso

occu

rred

as

non

-ski

n pi

xels

Skin

Dis

trib

utio

n in

HS

spac

eN

on-S

kin

in H

S sp

ace

Hue

Satu

rati

on

Out

line

Gene

ric

Skin

Mod

el

Gene

ric

Skin

Mod

el

Ada

ptiv

e Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Conc

lusi

ons

Conc

lusi

ons

Gene

ric

Skin

Mod

el

Stat

isti

c m

odel

s St

atis

tic

mod

els

His

togr

am (N

on P

aram

etri

c)H

isto

gram

(Non

Par

amet

ric)

Sing

le

Sing

le G

auss

ian

Gaus

sian

Mod

el (S

GM)

Mod

el (S

GM)

Gaus

sian

Gaus

sian

Mix

ture

Mod

el (G

MM

)M

ixtu

re M

odel

(GM

M)

Colo

r sp

aces

Colo

r sp

aces

Nor

mal

ized

(N

orm

aliz

ed (

rgrg, , H

SVH

SV, T

SL)

, TSL

) U

nU

n --no

rmal

ized

(RGB

)no

rmal

ized

(RGB

)

Expe

rim

enta

l Dat

abas

es

All

imag

es r

ando

mly

pic

ked

from

Web

Labe

ling

the

skin

reg

ion

man

ually

, usi

ng P

hoto

shop

75 m

illio

n75

mill

ion

24 m

illio

n24

mill

ion

554

554

TDSD

TD

SD (t

esti

ng d

atab

ase

(tes

ting

dat

abas

e fo

r sk

in d

etec

tion

)fo

r sk

in d

etec

tion

)

448

mill

ion

448

mill

ion

0015

0015

00N

SD

NSD

(non

(non

-- ski

n tr

aini

ng

skin

tra

inin

g da

taba

se)

data

base

)

0015

1 m

illio

n15

1 m

illio

n27

2027

20ST

D

STD

(s

kin

trai

ning

dat

abas

e)(s

kin

trai

ning

dat

abas

e)

Non

Non

-- Ski

n Sk

in

Skin

pix

els

Skin

pix

els

Imag

esIm

ages

Dat

aset

Dat

aset

ROC

Curv

es o

f Ge

neri

c Sk

in M

odel

sDetection Rate

His

togr

am

SGM

GMM Fa

lse

Posi

tive

Rat

e

Out

line

Gene

ric

Skin

Mod

elGe

neri

c Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Conc

lusi

ons

Conc

lusi

ons

Gene

ric

skin

mod

el

True

ski

n pi

xels

Fals

e sk

in p

ixel

s

Satu

rati

onH

ue

Skin

-Sim

ilar

Spac

e

Ada

ptiv

e sk

in m

odel

Skin

-Sim

ilar

Spac

e Ex

ampl

esTr

ue s

kin

–a

dom

inan

tGa

ussi

an

Fals

e sk

in –

a we

akGa

ussi

an

Two

Gaus

sian

sar

e se

para

ble

Satu

rati

onH

ue

Skin

Ga

ussi

an

Non

-Ski

n Ga

ussi

an

Satu

rati

onH

ue

Satu

rati

onH

ue

Skin

-Sim

ilar

Spac

eSk

in-S

imila

r Sp

ace

Skin

-Sim

ilar

Spac

e

Skin

Ga

ussi

anN

on-S

kin

Gaus

sian

Skin

Ga

ussi

anN

on-S

kin

Gaus

sian

Two-

Step

Ada

ptiv

e Fr

amew

ork

Gene

ric

Skin

Mod

el

Trai

ning

D

atas

et

Skin

-Sim

ilar

Pixe

lsN

on-S

kin

Pixe

ls

Gaus

sian

Mix

ture

Mod

el

EM L

earn

ing

an

Ada

ptiv

e Sk

in M

odel

True

-Ski

n Pi

xels

Fals

e-Sk

in

Pixe

ls

1st

Skin

Cla

ssif

icat

ion

2nd

Skin

Cla

ssif

icat

ion

Info

rmat

ion

spec

ific

to

the

imag

e un

der

cons

ider

atio

n

All

Imag

ePi

xels

Skin

-Sim

ilar

Spac

e

Info

rmat

ion

deri

ved

from

a

pre-

defi

ned

trai

ning

dat

abas

e

Why

two

ste

ps?

Colo

r di

stri

buti

on o

f al

l pix

els

in a

n im

age

Colo

r di

stri

buti

on o

f al

l pix

els

in a

n im

age

ofte

n sp

read

s ov

er t

he e

ntir

e sp

ace

ofte

n sp

read

s ov

er t

he e

ntir

e sp

ace

Har

der

to a

naly

ze a

nd m

odel

Har

der

to a

naly

ze a

nd m

odel

Colo

r di

stri

buti

on o

f Sk

inCo

lor

dist

ribu

tion

of

Skin

-- Sim

ilar

pixe

ls

Sim

ilar

pixe

ls

quit

e co

mpa

ct a

nd s

impl

equ

ite

com

pact

and

sim

ple

Effe

ctiv

e an

alys

is p

ossi

ble

Effe

ctiv

e an

alys

is p

ossi

ble

Divi

de o

ne h

ard

task

into

two

easi

er s

ubta

sks

EM B

ased

Ada

ptiv

e M

odel

ing

Step

1: O

btai

n an

Ini

tial

Gue

ss

Choo

sing

two

sep

arat

ed p

eaks

as

mea

nsCh

oosi

ng t

wo s

epar

ated

pea

ks a

s m

eans

A s

mal

l cov

aria

nce

mat

rix

is p

refe

rred

A s

mal

l cov

aria

nce

mat

rix

is p

refe

rred

Satu

rati

onH

ue

Skin

Ga

ussi

anN

on-S

kin

Gaus

sian

Satu

rati

on

Probability

Skin

-Sim

ilar

Spac

e

Star

ting

fro

m a

n in

itia

l gue

ss, u

pdat

e St

arti

ng f

rom

an

init

ial g

uess

, upd

ate

the

GMM

par

amet

ers

(th

e GM

M p

aram

eter

s ( w

eigh

twe

ight

, , mea

nm

ean

and

and

cova

rian

ceco

vari

ance

mat

rix

mat

rix )

as

follo

ws:

) as

follo

ws:

1

1 =

(

,)

N

Nnew

gl

ii

wplx

=

Θ∑

EM B

ased

Ada

ptiv

e M

odel

ing

Step

1: S

tand

ard

EM L

earn

ing

1 1

(,

) =

(

,)

Ng

ii

new

il

Ng

ii

xplx

uplx

= =

Θ Θ

∑ ∑

1

1

(,

)()(

) =

(

,)

Ng

new

new

Ti

il

il

new

il

Ng

ii

plx

xu

xu

plx

=

=

Θ−

−Σ

Θ

∑

∑

EM B

ased

Ada

ptiv

e M

odel

ing

Step

2: I

dent

ify

the

Skin

Gau

ssia

nEx

trac

ting

fea

ture

s fo

r pi

xels

wit

hin

each

Ex

trac

ting

fea

ture

s fo

r pi

xels

wit

hin

each

Ga

ussi

anGa

ussi

anGr

oup

AGr

oup

Afe

atur

es (

feat

ures

( Gau

ssia

nGa

ussi

andi

stri

buti

on r

elat

ed):

dist

ribu

tion

rel

ated

): we

ight

, mea

n an

d va

rian

ce.

weig

ht, m

ean

and

vari

ance

.

Grou

p B

Grou

p B

feat

ures

(spa

tial

and

sha

pe r

elat

ed):

feat

ures

(spa

tial

and

sha

pe r

elat

ed):

spre

adne

sssp

read

ness

, elo

ngat

ion,

his

togr

ams

on X

, elo

ngat

ion,

his

togr

ams

on X

-- dir

ecti

on

dire

ctio

n an

d Y

and

Y --di

rect

ion.

di

rect

ion.

An

An

SVM

SVM

clas

sifi

er is

tra

ined

to

iden

tify

the

cl

assi

fier

is t

rain

ed t

o id

enti

fy t

he

skin

skin

-- Gau

ssia

nGa

ussi

an, i

.e. o

ur a

dapt

ive

skin

mod

el, i

.e. o

ur a

dapt

ive

skin

mod

el

Skin

Det

ecti

on R

esul

ts--

-RO

C cu

rves

Det

ecti

on R

ate

False Positive

Ada

ptiv

e Sk

in-M

odel

us

ing

Grou

p A

+B

Gene

ric

GMM

Ski

n-M

odel

Ada

ptiv

e Sk

in-M

odel

on

ly u

sing

Gro

up A

FP (9

0% D

R):

Red:

22.

3

Gree

n: 2

7.6

Blue

: 32.

7

Skin

Det

ecti

on f

or S

till

Imag

es

Skin

Det

ecti

on f

or S

till

Imag

es

Skin

Det

ecti

on f

or S

till

Imag

es

Out

line

Gene

ric

Skin

Mod

elGe

neri

c Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Conc

lusi

ons

Conc

lusi

ons

Obj

ecti

onab

le I

mag

e Fi

lter

ing

SVM

as

the

base

cla

ssif

ier

A v

ecto

r of

144

fea

ture

s fo

r on

e im

age

colo

r hi

stog

ram

s, m

eans

, var

ianc

es, e

long

atio

n, a

nd

spre

adne

ssfr

om 1

2 na

tura

l col

or c

hann

els

text

ure

feat

ures

in t

hree

ori

enta

tion

s

skin inf

ormat

ion

as t

he 1

3th

colo

r ch

anne

l

15,0

00 o

bjec

tion

able

imag

es, a

nd 1

5,00

0 be

nign

imag

es a

s tr

aini

ng/t

esti

ng d

atab

ase

Usi

ng S

ymm

etri

c Sk

in-m

odel

Clas

sifi

er1

Clas

sifi

er1 (

C1):

(C1)

: App

ly

App

ly G

SMGS

Mto

bot

h ty

pes

of im

ages

to b

oth

type

s of

imag

esCl

assi

fier

2Cl

assi

fier

2 (C2

):(C

2): A

pply

A

pply

ASM

ASM

to b

oth

type

s of

imag

esto

bot

h ty

pes

of im

ages

Beni

gn im

ages

, i.e

. neg

ativ

e sa

mpl

esO

bjec

tion

able

imag

es,

i.e. p

osit

ive

sam

ples

Gene

ric

Skin

Mod

el

(GSM

)A

dapt

ive

Skin

Mod

el

(ASM

)

C1: u

sing

GSM

C2: u

sing

ASM

C1: u

sing

GSM

C2: u

sing

ASM

92.7

5/7.

1992

.75/

7.19

C2C291

.7/8

.29

91.7

/8.2

9C1C1

Det

ecti

on R

ate

/ Fa

lse

Posi

tive

Det

ecti

on R

ate

/ Fa

lse

Posi

tive

Type

Type

Usi

ng A

sym

met

ric

Skin

-mod

elCl

assi

fier

3Cl

assi

fier

3 (C3

):(C

3): G

SMGS

Mto

obj

ecti

onab

le a

nd

to o

bjec

tion

able

and

ASM

ASM

to b

enig

nto

ben

ign

Clas

sifi

er4

Clas

sifi

er4 (

C4):

(C4)

: ASM

ASM

to o

bjec

tion

able

and

to

obj

ecti

onab

le a

nd G

SMGS

Mto

ben

ign

to b

enig

n

Test

1:

Test

1: u

se s

kin

mod

els

in t

he s

ame

orde

r fo

r tr

aini

ng a

nd t

esti

ng s

tage

use

skin

mod

els

in t

he s

ame

orde

r fo

r tr

aini

ng a

nd t

esti

ng s

tage

Test

2:

Test

2: u

se s

kin

mod

els

in t

he r

ever

se o

rder

for

tra

inin

g an

d te

stin

g st

use

skin

mod

els

in t

he r

ever

se o

rder

for

tra

inin

g an

d te

stin

g st

age

age

76.8

/23.

476

.8/2

3.4

93.4

/6.6

193

.4/6

.61

C4C475

.6/2

4.5

75.6

/24.

594

.7/5

.33

94.7

/5.3

3C3C3

Test

2Te

st 2

Test

1Te

st 1Det

ecti

on R

ate

/ Fa

lse

Posi

tive

Det

ecti

on R

ate

/ Fa

lse

Posi

tive

Type

Type

••GS

M

GSM

and

an

d A

SMA

SMha

ve d

iffe

rent

impa

cts

to d

iffe

rent

ha

ve d

iffe

rent

impa

cts

to d

iffe

rent

ty

pes

of im

ages

, and

tha

t di

ffer

ence

may

bri

ng e

xtra

ty

pes

of im

ages

, and

tha

t di

ffer

ence

may

bri

ng e

xtra

se

para

ble

feat

ures

to

SVM

sepa

rabl

e fe

atur

es t

o SV

M

Two-

step

Hie

rarc

hica

l Bag

ging

Imag

eC2

Posi

tive

Neg

ativ

e

C3/T

est1

Fina

l Res

ult

C3/T

est2

C4/T

est1

C4/T

est2

Step

1St

ep 2

3141

3242

22

((

)(

))(

()

())

(1)

22

HBC

CPC

PC

PC

PC

PP

P+

+=

×+

−×

The

hope

is:w

e ge

t a

good

‘pri

or’k

nowl

edge

in S

tep1

, an

d th

us in

Ste

p2, T

est1

will

be

chos

en w

ith

high

pos

sibi

lity

Hig

h Co

nfid

ence

Prio

r kn

owle

dge

Imag

e Cl

assi

fica

tion

Res

ults

C1 u

sing

GSM

Hie

rarc

hica

l Bag

ging

wit

h a

com

bina

tion

of

C2, C

3 an

d C4

C2 u

sing

ASM

ROCs

for

Obj

ecti

onab

le I

mag

e Cl

assi

fica

tion

Ave

rage

Com

puta

tion

Ana

lysi

s (t

otal

300

0 im

ages

, 384

×25

6 si

ze)

301.0

0 m

s30

1.00

ms

84.8

5 m

s84

.85

ms

216.

15 m

s21

6.15

ms

HB

HB

266.

30 m

s26

6.30

ms

50.15

ms

50.15

ms

216.

15 m

s21

6.15

ms

C4C4

266.

59 m

s26

6.59

ms

50.4

1 m

s50

.41

ms

216.

15 m

s21

6.15

ms

C3C3

277.

44 m

s27

7.44

ms

61.2

9 m

s61

.29

ms

216.

15 m

s21

6.15

ms

C2C2

192.

26 m

s19

2.26

ms

60.4

7 m

s60

.47

ms

129.

81 m

s12

9.81

ms

C1C1

Ove

rall

Ove

rall

Imag

e Im

age

Clas

sifi

cati

onCl

assi

fica

tion

Feat

ure

Feat

ure

Extr

acti

onEx

trac

tion

Type

Type

Out

line

Out

line

Gene

ric

Skin

Mod

elGe

neri

c Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Ada

ptiv

e Sk

in M

odel

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Obj

ecti

onab

le I

mag

e Fi

lter

ing

Conc

lusi

ons

Conc

lusi

ons

Conc

lusi

ons

A t

woA

two

-- ste

p ad

apti

ve a

ppro

ach

step

ada

ptiv

e ap

proa

ch

Skin

Skin

-- Sim

ilar

Sim

ilar

Spac

eSp

ace

EMEMba

sed

Ada

ptiv

e M

odel

ing

base

d A

dapt

ive

Mod

elin

g

Succ

essf

ul a

pplic

atio

n to

Su

cces

sful

app

licat

ion

to o

bjec

tob

ject

--io

nabl

eio

nabl

eim

age

filt

erin

gim

age

filt

erin

g

Than

k yo

u !

Than

k yo

u !