an adaptive skin model and its application to ...lbmedia.ece.ucsb.edu/resources/ref/acm04ppt.pdf ·...
TRANSCRIPT
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 !