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I
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6
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6930
T
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Vo
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19
,
No
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6
,
Dec
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b
er
2
0
2
1
:
1
9
7
5
-
1981
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te
m
s
ar
e
r
etr
iev
al
ti
m
e
an
d
ef
f
icie
n
c
y
.
C
o
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
te
m
is
co
n
s
id
er
ed
to
b
e
ef
f
ec
ti
v
e
o
n
l
y
w
h
e
n
th
e
b
etter
r
esu
lts
ar
e
ac
h
iev
ed
in
a
s
h
o
r
t
s
p
an
o
f
ti
m
e
[
4
]
.
So
m
e
o
f
th
e
n
o
te
w
o
r
th
y
ap
p
licatio
n
s
o
f
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
te
m
s
ar
e
b
io
m
etr
ic
s
y
s
te
m
s
,
m
ed
ical
ap
p
li
ca
tio
n
s
,
a
n
d
tex
til
e
in
d
u
s
tr
y
a
n
d
s
o
o
n
.
T
h
e
b
io
m
etr
ic
ap
p
licatio
n
s
u
tili
ze
f
i
n
g
er
p
r
i
n
t,
p
al
m
p
r
in
t,
f
ac
e
i
m
ag
e
s
a
s
i
n
p
u
t
f
o
r
r
etr
iev
in
g
t
h
e
m
atch
in
g
e
n
tit
y
.
T
h
is
k
in
d
o
f
ap
p
licatio
n
s
ca
n
b
e
e
m
p
lo
y
ed
to
e
n
s
u
r
e
s
ec
u
r
it
y
[
5
]
.
T
h
e
co
n
ten
t
-
b
ased
i
m
ag
e
r
etr
i
v
al
s
y
s
te
m
s
in
m
ed
ical
f
ield
s
u
p
p
o
r
t
th
e
h
ea
l
th
ca
r
e
p
r
o
f
es
s
io
n
al
i
n
r
elatin
g
b
et
w
ee
n
th
e
s
i
m
ilar
ca
s
e
s
.
T
h
u
s
,
t
h
e
m
ed
ical
co
n
ten
t
b
ased
i
m
ag
e
r
e
tr
iv
al
s
y
s
te
m
s
h
elp
i
n
ac
h
iev
i
n
g
b
etter
d
iag
n
o
s
is
.
T
ex
tile
in
d
u
s
tr
ie
s
e
m
p
lo
y
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
t
e
m
s
f
o
r
f
in
d
i
n
g
t
h
e
r
elate
d
f
a
b
r
ic
im
a
g
es
.
w
h
ic
h
ar
e
r
ich
in
te
x
t
u
r
e.
A
n
e
w
co
n
ten
t
-
b
ased
i
m
a
g
e
r
etr
ie
v
al
m
e
t
h
o
d
in
t
h
at
te
x
tu
r
e
a
n
d
co
lo
r
f
ea
tu
r
e
u
s
ed
.
I
n
th
e
co
lo
r
im
ag
e
t
w
o
t
y
p
es
o
f
in
f
o
r
m
at
io
n
is
ex
tr
ac
ted
s
u
c
h
as
co
lo
r
an
d
tex
tu
r
e
f
ea
tu
r
e
.
I
n
w
h
ic
h
it
is
m
o
r
e
ac
cu
r
ate
f
o
r
i
m
a
g
e
r
etr
iev
al
b
ased
u
p
o
n
th
eir
q
u
er
y
r
eq
u
e
s
t
[
6
]
.
B
y
co
m
p
ar
in
g
to
th
e
co
n
v
en
t
io
n
al
m
o
m
e
n
ts
,
th
e
Z
er
n
ik
e
m
o
m
e
n
ts
h
a
s
less
s
en
s
iti
v
e
to
n
o
is
e
in
th
e
d
escr
ip
to
r
f
o
r
id
ea
l r
eg
io
n
-
b
ased
s
h
a
p
e.
R
ed
g
r
ee
n
b
lu
e
(
R
GB
)
im
a
g
e
co
n
v
er
ted
f
r
o
m
t
h
e
s
p
o
t
w
h
er
e
h
is
o
p
p
o
n
en
t
's
ch
r
o
m
at
icit
y
s
p
ac
e,
th
e
co
n
ten
t
s
o
f
th
e
ch
ar
ac
ter
is
tic
s
o
f
th
e
co
lo
r
o
f
an
i
m
a
g
e
ca
u
g
h
t
u
s
i
n
g
d
is
tr
ib
u
tio
n
m
o
m
e
n
t
s
o
f
Z
er
n
i
k
e
ch
r
o
m
a
ticit
y
[
7
]
.
T
h
e
m
ar
g
i
n
o
f
v
ar
iatio
n
o
f
th
e
r
o
tatio
n
an
d
s
ca
le
in
v
ar
ia
n
t
i
m
a
g
e
d
o
m
ai
n
d
escr
ip
tio
n
o
f
th
e
s
y
s
te
m
f
ea
tu
r
e
s
ar
e
ex
tr
ac
ted
an
d
h
as
less
f
ea
t
u
r
e
v
ec
to
r
d
im
en
s
io
n
.
T
h
e
lo
w
le
v
el
i
m
a
g
e
f
ea
t
u
r
es
d
ep
en
d
o
n
tex
tu
r
e,
s
h
ap
e
an
d
co
lo
r
in
th
e
co
n
te
n
t
–
b
ased
i
m
ag
e
r
etr
iv
al
s
y
s
te
m
.
O
n
e
o
f
th
e
m
ai
n
d
r
a
w
b
ac
k
s
o
f
t
h
e
co
n
ten
t
b
ased
i
m
ag
e
r
etr
iv
al
s
y
s
te
m
u
s
es
i
m
ag
e
s
o
f
s
i
m
ilar
lo
w
-
le
v
el
f
ea
t
u
r
es
to
v
ar
y
t
h
e
q
u
er
y
i
m
a
g
e
b
ased
o
n
th
e
o
b
j
ec
ts
th
at
th
e
u
s
er
i
s
p
r
ed
ictin
g
,
an
d
t
h
e
co
m
p
lex
i
t
y
is
id
e
n
ti
f
ied
as
t
h
i
s
t
y
p
e
o
f
s
e
m
a
n
tic
s
p
ac
e.
I
n
r
e
ce
n
t
ti
m
es,
co
n
te
n
t
b
ased
im
a
g
e
r
etr
iv
al
r
esear
ch
ef
f
o
r
t
i
n
th
e
lo
w
-
le
v
el
v
i
s
u
al
f
ea
t
u
r
es
an
d
h
i
g
h
-
le
v
el
s
e
m
a
n
tic
g
ap
is
r
ed
u
cin
g
b
et
w
ee
n
o
b
j
e
cts
in
th
e
i
m
ag
e
[
8
]
.
Sp
atial
c
o
m
m
u
n
ica
tio
n
asp
ec
t,
ap
p
r
o
x
i
m
atio
n
p
o
l
y
g
o
n
-
s
h
ap
ed
f
ea
tu
r
es
,
m
o
m
e
n
t
s
,
s
h
ap
e
-
s
p
ac
e
p
atter
n
s
an
d
ch
a
n
g
e
t
h
e
s
ize
ar
e
ex
tr
ac
ted
u
s
i
n
g
s
p
ac
e
f
ea
t
u
r
e
.
T
h
e
s
i
m
ilar
it
y
o
f
i
m
a
g
es
is
to
b
e
ca
lcu
late
t
h
e
v
ar
io
u
s
d
is
ta
n
ce
m
ea
s
u
r
es
f
o
r
s
em
a
n
tic
g
ap
,
an
d
d
is
cu
s
s
ab
o
u
t
th
e
r
etr
iev
al
o
f
in
v
ar
ian
t
i
m
ag
e.
T
h
e
s
i
m
il
ar
it
y
o
f
t
w
o
i
m
a
g
es
ca
n
b
e
o
b
tain
ed
b
y
m
ea
s
u
r
i
n
g
t
h
e
d
is
t
an
ce
v
al
u
e
b
et
w
ee
n
th
e
m
[
9
]
,
[
10
]
.
On
e
o
f
th
e
u
n
s
u
p
er
v
i
s
ed
lear
n
in
g
tech
n
iq
u
e
is
i
m
ag
e
cl
u
s
ter
i
n
g
.
Fo
r
an
y
p
ar
ticu
lar
p
r
o
b
lem
ca
n
n
o
t
b
e
s
ep
ar
ated
o
n
th
e
b
a
s
is
o
f
a
n
o
v
el
m
u
lti
-
d
i
m
en
s
i
o
n
al
lif
ti
n
g
s
ch
e
m
atic
s
tr
u
ct
u
r
e
o
f
th
e
b
an
d
w
id
t
h
f
ilter
b
an
k
d
is
c
u
s
s
ed
[
11
]
,
[
12
]
.
T
h
e
co
n
ten
t
-
b
ased
r
etr
iev
al
is
w
o
r
k
ed
w
it
h
t
y
p
e
s
o
f
i
m
ag
es,
p
atter
n
s
o
f
u
s
e,
th
e
s
e
n
s
o
r
y
g
ap
an
d
t
h
e
r
o
le
o
f
s
e
m
a
n
tics
[
1
3
]
,
[
1
4
]
.
Ob
j
ec
t
an
d
s
h
ap
e
f
ea
tu
r
es.
E
ac
h
f
ea
t
u
r
e
t
y
p
es
t
h
e
s
i
m
ilar
it
y
o
f
o
b
j
ec
ts
an
d
p
ictu
r
es
ar
e
r
ev
ie
w
ed
,
t
h
r
o
u
g
h
i
n
ter
ac
tio
n
w
i
th
th
e
f
ee
d
b
ac
k
o
f
th
e
u
s
er
s
o
f
t
h
e
s
y
s
te
m
s
a
n
d
m
et
h
o
d
s
ar
e
ca
p
ab
le
o
f
p
r
o
d
u
cin
g
i
n
clo
s
e
co
n
tact
w
it
h
it.
C
o
n
te
n
t
-
b
ased
r
etr
iev
al
ap
p
licatio
n
s
ar
e
d
is
cu
s
s
ed
i
n
th
r
ee
b
r
o
ad
ca
teg
o
r
ies:
as
s
o
ciatio
n
s
ea
r
c
h
,
ta
r
g
et
s
ea
r
ch
,
a
n
d
ca
teg
o
r
y
s
ea
r
ch
.
2.
O
B
J
E
CT
I
V
E
S O
F
T
H
E
R
E
SE
ARCH
T
h
e
m
a
in
o
b
je
c
t
iv
e
o
f
th
e
r
e
s
e
a
r
ch
i
s
t
o
p
r
es
en
t
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
t
em
s
f
o
r
th
e
u
l
t
r
a
s
o
u
n
d
im
ag
es
o
f
th
e
k
i
d
n
ey
.
I
n
o
r
d
e
r
t
o
a
t
ta
in
th
is
o
b
je
c
ti
v
e
,
th
e
en
t
i
r
e
r
e
s
ea
r
c
h
w
o
r
k
i
s
d
i
v
i
d
e
d
i
n
t
o
th
r
e
e
s
e
p
a
r
a
t
e
p
h
as
e
s
an
d
e
a
ch
p
h
as
e
w
o
r
k
s
t
o
w
a
r
d
s
a
ch
ie
v
in
g
th
e
r
es
e
a
r
ch
g
o
a
l
[
1
4
]
.
T
h
e
o
b
je
c
t
i
v
es
o
f
e
a
ch
an
d
e
v
e
r
y
p
h
a
s
e
a
r
e
l
is
t
e
d
b
el
o
w
.
T
o
p
r
o
p
o
s
e
a
co
n
ten
t
b
ased
i
m
ag
e
r
etr
i
v
al
s
y
s
t
em
w
i
th
b
ett
e
r
f
ea
tu
r
e
s
e
l
e
ct
i
o
n
m
o
d
e
l
f
o
r
u
lt
r
a
s
o
u
n
d
im
ag
es
o
f
k
i
d
n
ey
.
T
h
e
i
d
e
a
o
f
th
is
p
h
as
e
is
t
o
r
e
d
u
c
e
th
e
c
o
m
p
u
t
at
i
o
n
a
l
c
o
m
p
l
ex
i
ty
an
d
t
im
e
c
o
n
s
u
m
p
t
i
o
n
o
f
th
e
co
n
t
en
t
b
ased
i
m
ag
e
r
etr
iv
al
s
y
s
tem
[
1
5
]
,
[
1
6
]
.
T
o
p
r
e
s
e
n
t
a
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
t
em
f
o
r
u
l
t
r
as
o
u
n
d
i
m
ag
es
o
f
k
i
d
n
ey
b
y
m
e
an
s
o
f
as
s
o
ci
a
t
i
o
n
r
u
l
e
m
in
in
g
c
l
as
s
if
i
ca
t
i
o
n
t
e
ch
n
i
q
u
e
.
I
n
t
h
is
p
h
as
e
,
th
e
a
s
s
o
c
i
at
i
o
n
r
u
les
a
r
e
g
en
e
r
a
t
e
d
,
o
r
g
a
n
i
z
e
d
a
n
d
t
h
en
u
ti
l
iz
e
d
t
o
p
e
r
f
o
r
m
c
la
s
s
if
i
ca
t
i
o
n
[
1
7
]
,
[
1
8
]
.
H
e
n
ce
,
th
e
o
b
je
c
t
iv
e
s
o
f
th
e
r
es
e
a
r
ch
w
o
r
k
a
r
e
p
r
es
en
t
e
d
a
n
d
th
e
f
o
ll
o
w
in
g
s
e
c
ti
o
n
p
r
e
s
en
t
s
th
e
o
v
e
r
a
ll
f
l
o
w
o
f
t
h
e
p
r
o
p
o
s
e
d
a
p
p
r
o
a
ch
.
Fig
u
r
e
1
o
v
e
r
a
l
l
s
t
r
u
ct
u
r
e
o
f
t
h
e
p
r
o
p
o
s
e
d
co
n
te
n
t b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
tem
.
Fig
u
r
e
1
.
Stru
ct
u
r
e
o
f
th
e
p
r
o
p
o
s
ed
C
B
I
R
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
S
u
p
o
r
t v
is
u
a
l d
et
a
ils
o
f X
-
r
a
y
ima
g
e
w
ith
p
la
in
in
fo
r
ma
tio
n
(
N
a
s
h
w
a
n
Ja
s
im
Hu
s
s
ein
)
1977
3.
P
RO
P
O
SE
D
CB
I
R
SY
ST
E
M
T
h
is
p
r
o
p
o
s
es
a
n
e
w
f
ea
tu
r
e
s
elec
tio
n
m
et
h
o
d
ca
lled
"
I
I
C
B
Me
r
g
eFS
"
.
T
h
is
h
elp
s
to
i
m
p
r
o
v
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
C
B
I
R
s
y
s
te
m
b
y
u
s
i
n
g
s
tab
le
f
ea
tu
r
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s
elec
tio
n
th
r
o
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g
h
d
is
cr
etiza
t
io
n
f
o
r
u
ltra
s
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n
d
k
id
n
e
y
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m
a
g
e
d
iag
n
o
s
i
s
.
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h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
ex
tr
ac
ts
th
e
lo
w
-
le
v
el
f
ea
tu
r
es
b
ase
d
o
n
th
e
h
ig
h
-
le
v
el
k
n
o
w
led
g
e,
in
o
r
d
er
to
s
u
g
g
est
a
b
etter
d
iag
n
o
s
is
f
o
r
th
e
q
u
er
y
i
m
ag
e
s
.
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h
e
alg
o
r
it
h
m
o
f
t
h
is
w
o
r
k
is
p
r
esen
ted
as
f
o
llo
w
s
.
T
h
e
o
v
er
all
s
tr
u
ctu
r
e
o
f
t
h
e
n
e
w
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
al
s
y
s
t
e
m
is
p
r
esen
ted
in
alg
o
r
ith
m
1
.
T
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
is
s
u
b
d
iv
id
ed
in
to
t
h
r
e
e
p
h
ases
,
a
s
lis
ted
:
i
)
f
ea
t
u
r
e
s
elec
tio
n
b
ased
o
n
I
I
C
B
Me
r
g
eFS,
ii
)
I
I
C
B
Me
r
g
eFS
b
ased
ass
o
ciatio
n
r
u
le
m
i
n
in
g
,
an
d
iii
)
clas
s
i
f
icatio
n
u
s
in
g
co
n
v
o
lu
tio
n
al
n
eu
r
al
n
et
w
o
r
k
(
C
NN)
alg
o
r
i
th
m
Fi
g
u
r
e
1
.
Ov
er
al
l
s
tr
u
ctu
r
e
o
f
th
e
p
r
o
p
o
s
ed
co
n
ten
t
b
ased
i
m
a
g
e
r
etr
iv
a
l
s
y
s
te
m
e
x
p
lai
n
t
h
e
p
r
o
ce
d
u
r
e
o
f
i
m
a
g
e
a
n
al
y
s
i
s
f
ea
tu
r
e
e
x
tr
ac
tio
n
.
Fo
llo
w
in
g
s
u
b
s
ec
tio
n
s
d
escr
ib
e
th
e
d
etails
ex
p
lan
atio
n
o
f
t
h
ese
m
o
d
u
les.
A
l
g
o
r
ith
m
1
P
r
o
p
o
s
ed
C
B
I
R
S
y
s
te
m
Procedure Overall
Input: Image
database
Result: Relevant images with
classes Training Pha
se:
For all Images do
Pre
-
Process the images
Extract texture features from the training images
End for
Execute IICBMergeFS algorithm Mine
Association Rules
Test Phase:
Extract texture features from the test image
Classify the images by applying K
-
Nearest
Neighbour (CNN) algorithm
Return the relevant images and class name found
4.
E
XP
E
RM
E
NT
AL
R
E
SU
L
T
AND
DIS
CUS
I
O
N
A
ll
th
e
p
ict
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r
e
c
u
ts
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ce
s
s
ib
le
in
d
atab
ase
ar
e
u
n
d
er
p
iv
o
tal
p
o
in
t o
f
v
ie
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w
i
th
th
e
f
r
a
m
e
w
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r
k
s
ize
o
f
2
5
6
×2
5
6
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r
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1
2
×5
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2
an
d
1
6
b
its
f
o
r
ev
er
y
p
ix
el.
At
t
h
e
p
r
esen
t
ti
m
e,
t
h
e
s
u
p
p
o
r
t
-
b
as
ed
d
ata
-
s
et
(
SB
D
)
co
n
tain
s
e
m
u
lated
m
i
n
d
M
R
I
d
ata
in
p
er
s
p
ec
tiv
e
o
f
t
w
o
a
n
ato
m
ica
l
m
o
d
els
:
n
o
r
m
al
a
n
d
m
u
l
tip
le
s
cler
o
s
i
s
(
MS)
.
Fo
r
b
o
th
o
f
th
ese,
f
u
ll
3
-
d
i
m
e
n
s
io
n
al
in
f
o
r
m
atio
n
v
o
lu
m
es
h
a
v
e
b
ee
n
r
ee
n
ac
t
ed
u
tili
zin
g
t
h
r
ee
ar
r
an
g
e
m
en
t
s
(
T
1
-
,
T
2
-
,
an
d
p
r
o
to
n
d
en
s
it
y
(
P
D
-
)
w
e
i
g
h
te
d
)
an
d
an
a
s
s
o
r
t
m
en
t
o
f
c
u
t
t
h
ick
n
e
s
s
es,
cla
m
o
r
lev
els,
a
n
d
le
v
els
o
f
p
o
w
er
non
-
co
n
s
is
ten
c
y
.
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n
Fi
g
u
r
e
2
s
a
m
p
le
1
0
class
e
s
i
f
C
B
I
R
,
r
e
tr
iev
ed
i
m
a
g
e
w
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t
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co
n
clu
s
io
n
m
atr
ix
f
o
r
C
B
I
R
l
u
n
g
i
m
ag
e
e
x
p
lai
n
ed
in
Fi
g
u
r
e
3
.
A
n
d
co
m
p
ar
ativ
e
a
n
al
y
s
i
s
b
ased
o
n
ac
cu
r
ac
y
,
s
en
s
iti
v
it
y
a
n
d
s
p
ec
i
f
icit
y
o
f
v
ar
io
u
s
f
ea
t
u
r
e
s
elec
t
io
n
p
r
esen
ted
in
T
ab
le
1
.
Fig
u
r
e
2.
Sa
m
p
le
1
0
class
es i
f
C
B
I
R
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l
C
o
n
tr
o
l
,
Vo
l.
19
,
No
.
6
,
Dec
em
b
er
2
0
2
1
:
1
9
7
5
-
1981
1978
Fig
u
r
e
3
.
C
lass
if
ier
's ac
cu
r
ac
y
an
d
er
r
o
r
c
o
m
p
ar
is
o
n
s
T
ab
le
1
.
C
o
m
p
ar
ativ
e
a
n
al
y
s
i
s
b
ased
o
n
ac
cu
r
ac
y
,
s
en
s
iti
v
it
y
an
d
s
p
ec
if
icit
y
o
f
v
ar
io
u
s
f
ea
t
u
r
e
s
elec
tio
n
tech
n
iq
u
es
T
e
c
h
n
i
q
u
e
s
/
P
e
r
f
o
r
man
c
e
M
e
t
r
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c
c
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r
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(
%)
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C
A
I
M
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I
8
9
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8
6
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2
6
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I
8
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3
Her
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s
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to
t
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u
er
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ased
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u
er
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ted
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to
r
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s
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ti
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w
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ar
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n
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s
ed
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n
t
h
e
clas
s
i
f
icatio
n
o
r
r
etr
iev
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p
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o
ce
s
s
es
[
1
9
]
.
R
etr
i
ev
ed
i
m
ag
e
w
it
h
co
n
clu
s
io
n
m
atr
i
x
f
o
r
C
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I
R
lu
n
g
i
m
a
g
e
in
T
ab
le
2
an
d
r
ec
all
r
ates
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al
y
s
i
s
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y
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ar
y
i
n
g
f
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t
u
r
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s
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tio
n
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h
n
iq
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e
s
ex
p
l
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ed
in
T
ab
le
3
.
A
d
d
itio
n
ally
,
th
e
p
r
ec
is
io
n
a
n
d
r
ec
all
r
ates
o
f
t
h
e
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
ar
e
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alu
ated
a
n
d
t
h
e
r
esu
lt
s
ar
e
p
r
esen
ted
in
T
a
b
le
4
.
Fro
m
T
ab
le
4
,
it
is
co
n
clu
d
ed
th
at
t
h
e
p
r
o
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s
e
d
ap
p
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o
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m
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x
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m
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m
p
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ec
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d
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all
r
ates,
w
h
en
co
m
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ar
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e
ex
is
t
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g
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h
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e
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ad
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iti
o
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to
th
is
,
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h
e
ti
m
e
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n
s
u
m
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ti
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le
s
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er
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h
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n
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e
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i
s
ti
n
g
tec
h
n
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h
is
s
ec
tio
n
co
m
p
ar
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e
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er
f
o
r
m
an
ce
o
f
th
e
p
r
o
p
o
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ed
a
p
p
r
o
ac
h
b
y
v
ar
y
i
n
g
th
e
class
i
f
icatio
n
tech
n
iq
u
es
[
2
0
]
.
T
h
e
class
if
ier
s
b
ein
g
co
n
s
id
er
ed
f
o
r
p
er
f
o
r
m
a
n
ce
an
a
l
y
s
i
s
ar
e
d
escr
ea
te
w
a
v
elet
tr
an
s
f
o
r
m
,
s
u
p
p
o
r
t
v
ec
ter
m
ac
h
i
n
e
alg
o
r
it
h
m
a
n
d
co
n
v
o
l
u
tio
n
n
e
u
r
al
n
et
wo
r
k
class
i
f
ier
s
.
T
h
e
p
r
o
p
o
s
ed
f
ea
tu
r
e
s
elec
tio
n
t
ec
h
n
iq
u
e
I
I
C
B
Me
r
g
eFS
i
s
[
2
1
]
.
C
o
m
p
ar
ativ
e
an
al
y
s
is
b
ased
o
n
ac
cu
r
ac
y
,
s
en
s
iti
v
it
y
,
s
p
ec
i
f
icit
y
,
p
r
ec
is
i
o
n
an
d
r
ec
all
o
f
v
ar
io
u
s
class
i
f
icatio
n
tech
n
iq
u
es
p
r
esen
ted
in
T
ab
le
3
an
d
th
e
ti
m
e
co
m
p
le
x
it
y
o
f
t
h
e
clas
s
i
f
i
er
s
w
i
th
I
I
C
B
Me
r
g
e
FS
is
a
n
al
y
ze
d
an
d
th
e
r
es
u
lt
s
ar
e
p
r
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ted
in
T
ab
le
5
.
T
ab
le
2
.
R
etr
iev
ed
i
m
ag
e
w
i
th
co
n
clu
s
io
n
m
atr
ix
f
o
r
C
B
I
R
l
u
n
g
i
m
ag
e
4
.
1
7
% (
1)
0
0
0
0
4
.
1
7
% (
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4
.
1
7
% (
1)
0
0
8
7
.
6
(
2
1
)
0
0
0
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0
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0
8
4
.
6
(
1
8
)
0
5
.
2
8
%
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0
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8
6
.
6
(
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0
)
1
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5
3
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4
.
9
(
1
9
)
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6
(
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0
.
5
)
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2
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6
(
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)
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3
.
6
(
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8
)
0
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3
.
6
(
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8
)
0
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5
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3
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8
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6
(
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0
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0
1
0
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5
3
%
1
0
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0
0
0
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0
0
0
T
ab
le
3
.
P
r
ec
is
io
n
an
d
r
ec
all
r
ates a
n
al
y
s
i
s
b
y
v
ar
y
i
n
g
f
ea
t
u
r
e
s
elec
tio
n
tec
h
n
iq
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es
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T
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I
SS
N
:
1
6
9
3
-
6930
T
E
L
KOM
NI
K
A
T
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19
,
No
.
6
,
Dec
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2
0
2
1
:
1
9
7
5
-
1981
1980
RE
F
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R
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NC
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]
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.
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]
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v
e
let
T
ra
n
s
f
o
r
m
,
”
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
mp
u
ti
n
g
,
C
o
mm
u
n
ica
ti
o
n
a
n
d
Ne
two
rk
in
g
,
IEE
E,
2
0
0
8
,
p
p
.
1
-
1
6
,
d
o
i:
1
0
.
1
1
0
9
/ICCCNET
.
2
0
0
8
.
4
7
8
7
7
3
4
.
[3
]
S
.
H
.
Ja
d
h
a
v
a
n
d
S
.
A
.
A
h
m
e
d
,
“
Co
n
ten
t
b
a
se
d
im
a
g
e
re
tri
e
v
a
l
s
y
s
tem
w
it
h
h
y
b
rid
f
e
a
tu
re
se
t
a
n
d
r
e
c
e
n
tl
y
re
tri
e
v
e
d
im
a
g
e
li
b
ra
r
y
,”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Co
m
p
u
ter
A
p
p
li
c
a
ti
o
n
s
,
v
o
l.
5
9
,
n
o
.
5
,
pp
.
4
6
-
5
5
,
2
0
1
2
,
d
o
i:
1
0
.
5
1
2
0
/9
5
4
8
-
4
0
0
1
.
[4
]
A
.
M
u
m
taz
,
S
.
A
.
M
.
G
il
a
n
i,
a
n
d
T
.
Ja
m
e
e
l,
“
A
n
o
v
e
l
te
x
tu
re
i
m
a
g
e
re
tri
e
v
a
l
s
y
ste
m
b
a
s
e
d
o
n
d
u
a
l
tre
e
c
o
m
p
lex
w
a
v
e
let
tran
sf
o
r
m
a
n
d
su
p
p
o
r
t
v
e
c
to
r
m
a
c
h
in
e
s
,”
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Eme
rg
i
n
g
T
e
c
h
n
o
l
o
g
ies
,
2
0
0
6
,
p
p
.
1
0
8
-
114
,
d
o
i
:
1
0
.
1
1
0
9
/ICE
T
.
2
0
0
6
.
3
3
5
9
1
0
.
[5
]
S.
S
o
m
a
n
,
M
.
G
h
o
r
p
a
d
e
,
V
.
S
o
n
o
n
e
,
a
n
d
S
.
Ch
a
v
a
n
,
“
Co
n
ten
t
b
a
se
d
im
a
g
e
re
tri
e
v
a
l
u
sin
g
a
d
v
a
n
c
e
d
c
o
lo
r
a
n
d
tex
tu
re
f
e
a
tu
re
s
,”
In
ter
n
a
ti
o
n
a
l
C
o
n
fer
e
n
c
e
in
Co
m
p
u
t
a
ti
o
n
a
l
I
n
tel
li
g
e
n
c
e
(
ICCIA)
,
v
o
l.
3
,
n
o
.
4
,
2
0
1
2
,
p
p
.
1
-
5
,
d
o
i
:
1
0
.
1
3
1
4
0
/2
.
1
.
5
0
9
2
.
0
0
0
1
.
[6
]
N.
Da
lal
a
n
d
B.
T
rig
g
s
“
Histo
g
ra
m
s
o
f
o
rien
ted
g
ra
d
ien
ts
f
o
r
h
u
m
a
n
d
e
tec
ti
o
n
,
”
IEE
E
C
o
mp
u
ter
S
o
c
iety
Co
n
fer
e
n
c
e
o
n
C
o
mp
u
ter
Vi
sio
n
a
n
d
Pa
tt
e
rn
Rec
o
g
n
it
io
n
(
CVP
R'0
5
)
,
2
0
0
5
,
d
o
i
:
1
0
.
1
1
0
9
/C
VP
R.
2
0
0
5
.
1
7
7
.
[7
]
A
.
Am
a
n
a
ti
a
d
is,
V.
Ka
b
u
rlas
o
s
,
A
.
G
a
ste
ra
to
s,
a
n
d
S
.
E.
P
a
p
a
d
a
k
is
,
“
Ev
a
lu
a
ti
o
n
o
f
sh
a
p
e
d
e
sc
rip
to
rs
f
o
r
sh
a
p
e
-
b
a
se
d
im
a
g
e
re
tri
e
v
a
l
,
”
I
ET
Ima
g
e
Pro
c
e
ss
in
g
5
,
n
o
.
5
,
v
o
l.
5
8
,
p
p
.
4
9
3
-
4
9
9
,
2
0
1
1
,
doi
:
1
0
.
1
0
4
9
/i
e
t
-
ip
r.
2
0
0
9
.
0
2
4
6
.
[8
]
H.
A
s
so
d
ik
y
,
A
.
Ba
su
k
i,
a
n
d
F
.
F
.
Ha
rd
ian
sy
a
h
,
“
M
a
c
ro
-
siz
e
d
b
a
sid
io
m
y
c
o
ta
sp
e
c
ies
re
c
o
g
n
it
io
n
u
sin
g
sh
a
p
e
a
n
d
c
o
lo
r
f
e
a
tu
re
s,”
In
ter
n
a
ti
o
n
a
l
El
e
c
tro
n
ics
S
y
mp
o
si
u
m
(IE
S
),
2
0
1
6
,
p
p
.
3
0
9
-
3
1
4
,
doi
:
1
0
.
1
1
0
9
/
EL
ECS
YM.
2
0
1
6
.
7
8
6
1
0
2
3
.
[9
]
S.
L
i,
M
.
C
.
L
e
e
,
a
n
d
C
.
M
.
P
u
n
,
“
Co
m
p
lex
Zern
ik
e
m
o
m
e
n
ts
fe
a
tu
re
s
f
o
r
sh
a
p
e
-
b
a
se
d
im
a
g
e
r
e
tri
e
v
a
l,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
S
y
ste
ms
,
M
a
n
,
a
n
d
Cy
b
e
rn
e
ti
c
s
-
Pa
rt
A:
S
y
ste
ms
a
n
d
Hu
ma
n
s
,
v
o
l
.
3
9
,
no.
v
o
l
.
1
,
p
p
.
2
2
7
-
2
3
7
,
2
0
0
9
,
d
o
i
:
1
0
.
1
1
0
9
/T
S
M
CA
.
2
0
0
8
.
2
0
0
7
9
8
8
.
[1
0
]
P.
De
sa
i,
J
.
P
u
jari,
a
n
d
A
.
Kin
n
i
k
a
r
,
“
P
e
rf
o
rm
a
n
c
e
e
v
a
lu
a
ti
o
n
o
f
i
m
a
g
e
re
tri
e
v
a
l
s
y
st
e
m
s
u
sin
g
sh
a
p
e
f
e
a
tu
re
b
a
se
d
o
n
w
a
v
e
l
e
t
tran
sf
o
r
m
,
”
S
e
c
o
n
d
In
ter
n
a
t
io
n
a
l
Co
n
fer
e
n
c
e
o
n
Co
g
n
it
ive
Co
m
p
u
ti
n
g
a
n
d
In
f
o
rm
a
ti
o
n
Pr
o
c
e
ss
in
g
(
CCIP)
,
2
0
1
6
,
p
p
.
1
-
5,
doi
:
1
0
.
1
1
0
9
/CCI
P
.
2
0
1
6
.
7
8
0
2
8
7
6
.
[1
1
]
C
.
S
.
G
o
d
e
a
n
d
A
.
S
.
K
h
o
b
ra
g
a
d
e
,
“
Ob
jec
t
d
e
tec
ti
o
n
u
si
n
g
c
o
lo
r
c
lu
e
a
n
d
sh
a
p
e
f
e
a
tu
re
,
”
2
0
1
6
I
n
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
W
ire
les
s
Co
mm
u
n
ic
a
ti
o
n
s,
S
i
g
n
a
l
Pro
c
e
ss
in
g
a
n
d
Ne
two
rk
in
g
(
W
iS
PNE
T
)
,
2
0
1
6
,
doi
:
1
0
.
1
1
0
9
/W
iS
P
NET
.
2
0
1
6
.
7
5
6
6
1
7
7
.
[1
2
]
E
.
S
o
k
ic
a
n
d
S
.
K
o
n
ji
c
ij
a
,
“
No
v
e
l
f
o
u
rier
d
e
sc
rip
to
r
b
a
se
d
o
n
c
o
m
p
lex
c
o
o
rd
i
n
a
tes
sh
a
p
e
sig
n
a
tu
re
,”
In
ter
n
a
ti
o
n
a
l
W
o
rk
sh
o
p
o
n
C
o
n
te
n
t
-
B
a
se
d
M
u
lt
ime
d
ia
I
n
d
e
x
in
g
(
CBM
I),
IEE
E,
2
0
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/CBM
I.
2
0
1
4
.
6
8
4
9
8
4
3
4
.
[1
3
]
N.
A
lajlan
,
M
.
S
.
Ka
m
e
l,
a
n
d
G
.
H.
F
re
e
m
a
n
,
“
G
e
o
m
e
tr
y
-
b
a
se
d
im
a
g
e
re
tri
e
v
a
l
n
b
i
n
a
ry
i
m
a
g
e
d
a
tab
a
se
s,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
P
a
tt
e
rn
An
a
lys
is
a
n
d
M
a
c
h
i
n
e
I
n
telli
g
e
n
c
e
,
v
o
l.
3
0
,
n
o
.
6
,
p
p
.
1
0
0
3
-
1
0
1
3
,
2
0
0
8
,
doi
:
1
0
.
1
1
0
9
/T
P
A
M
I.
2
0
0
8
.
3
7
.
[1
4
]
C
.
Rig
a
u
d
,
D.
Ka
ra
tza
s,
J
.
C
.
Bu
rie
,
a
n
d
J
.
M
.
Og
iera
n
d
,
“
Co
lo
r
De
sc
rip
to
r
f
o
r
Co
n
ten
t
-
Ba
se
d
Dra
w
in
g
Re
tri
e
v
a
l
,”
1
1
t
h
IAP
R
In
ter
n
a
ti
o
n
a
l
W
o
rk
sh
o
p
o
n
Do
c
u
me
n
t
A
n
a
lys
is
S
y
ste
ms
,
2
0
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/DA
S
.
2
0
1
4
.
7
0
.
[1
5
]
E.
T
iak
a
s,
D
.
Ra
f
a
il
id
is,
A
.
Dim
o
u
,
a
n
d
P
.
Da
ra
s,
“
M
S
IDX
:
m
u
lt
i
-
so
rt
in
d
e
x
in
g
f
o
r
e
ff
icie
n
t
c
o
n
ten
t
-
b
a
se
d
im
a
g
e
se
a
rc
h
a
n
d
re
tri
e
v
a
l,
”
IEE
E
T
ra
n
s
a
c
ti
o
n
s
o
n
M
u
lt
ime
d
i
a
,
v
o
l.
15
,
n
o
.
6
,
p
p
.
1
4
1
5
-
1
4
3
0
,
2
0
1
3
,
doi
:
1
0
.
1
1
0
9
/T
M
M
.
2
0
1
3
.
2
2
4
7
9
8
9
.
[1
6
]
M.
Ka
n
,
D
.
Xu
,
S
.
S
h
a
n
,
a
n
d
X
.
Ch
e
n
,
“
S
e
m
isu
p
e
rv
ise
d
Ha
sh
in
g
v
ia
Ke
rn
e
l
Hy
p
e
rp
lan
e
Lea
rn
in
g
f
o
r
S
c
a
lab
le
Im
a
g
e
S
e
a
rc
h
,
”
IEE
E
T
ra
n
sa
c
ti
o
n
s o
n
Circ
u
it
s a
n
d
S
y
ste
ms
fo
r V
id
e
o
T
e
c
h
n
o
lo
g
y
,
v
o
l.
2
4
,
n
o
.
4
,
p
p
.
7
0
4
-
7
1
3
,
2
0
1
3
,
d
o
i:
1
0
.
1
1
0
9
/T
CS
V
T
.
2
0
1
3
.
2
2
7
6
7
1
3
.
[1
7
]
S
.
P
a
isi
tk
rian
g
k
ra
i,
C
.
S
h
e
n
,
a
n
d
A
.
v
a
n
d
e
n
He
n
g
e
l
,
“
L
a
r
g
e
-
M
a
rg
in
L
e
a
rn
in
g
o
f
Co
m
p
a
c
t
Bin
a
r
y
I
m
a
g
e
En
c
o
d
in
g
s
,”
EE
E
T
ra
n
sa
c
ti
o
n
s
o
n
Im
a
g
e
Pro
c
e
ss
in
g
,
v
o
l
.
23
,
n
o
.
9,
p
p
.
4
0
4
1
–
4
0
5
4
,
2
0
1
4
,
d
o
i
:
1
0
.
1
1
0
9
/T
I
P
.
2
0
1
4
.
2
3
3
7
7
5
9
.
[1
8
]
D
.
C
.
G
.
P
e
d
r
o
n
e
tt
e
,
R
.
T
.
Ca
lu
m
b
y
,
a
n
d
R
.
d
a
S
.
T
o
rre
s,
“
S
e
m
i
-
su
p
e
rv
ise
d
L
e
a
rn
in
g
f
o
r
Re
lev
a
n
c
e
F
e
e
d
b
a
c
k
o
n
Im
a
g
e
R
e
tri
e
v
a
l
T
a
s
k
s
,
”
S
IBGR
AP
I
C
o
n
fer
e
n
c
e
o
n
Gr
a
p
h
ics
,
Pa
tt
e
rn
s
a
n
d
Ima
g
e
s
,
2
0
1
4
,
doi
:
1
0
.
1
1
0
9
/S
IBG
R
A
P
I.
2
0
1
4
.
4
4
.
[1
9
]
J.
M
.
G
u
o
,
H
.
P
ra
se
t
y
o
,
a
n
d
J
.
H
.
Ch
e
n
,
”
Co
n
ten
t
-
Ba
se
d
Im
a
g
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Evaluation Warning : The document was created with Spire.PDF for Python.