TELK
OMNIKA
V
ol.
16,
No
.
3,
J
une
2018,
pp
.
1256
1263
ISSN:
1693-6930,
accredited
A
b
y
DIKTI,
Decree
No:
58/DIKTI/K
ep/2013
1256
W
a
velet-Based
Color
Histogram
on
Content-Based
Ima
g
e
Retrie
v
al
Ale
xander
,
Jeklin
Hare
fa*
,
Y
ud
y
Purnama
,
and
Har
vianto
Computer
Science
Depar
tment,
School
of
Computer
Science
,
Bina
Nusantar
a
Un
iv
ersity
,
J
akar
ta,
Indonesia
11480
*Corresponding
A
uthor
,
email:
jharef
a@bin
us
.edu
Abstract
The
g
ro
wth
of
image
databases
in
man
y
domains
,
including
f
ash
ion,
biometr
ic
,
g
r
aphic
design,
architecture
,
etc.
has
increased
r
apidly
.
Content
Based
Image
Retr
ie
v
al
System
(CBIR)
is
a
technique
used
f
or
finding
rele
v
ant
images
from
those
huge
and
unannotated
image
databases
based
on
lo
w-le
v
el
f
eatures
of
the
quer
y
images
.
In
this
study
,
an
attempt
to
emplo
y
2
nd
le
v
el
W
a
v
elet
Based
Color
Histog
r
am
(WBCH)
on
a
CBIR
system
is
proposed.
Image
database
used
in
this
study
are
tak
en
from
W
ang’
s
image
database
containing
1000
color
images
.
The
e
xper
iment
results
sho
w
that
2
nd
le
v
el
WBCH
giv
es
better
precision
(0.777)
than
the
other
methods
,
including
1
st
le
v
el
WBCH,
Color
Histog
r
am,
Color
Co-occurrence
Matr
ix,
and
W
a
v
elet
te
xture
f
eature
.
It
can
be
concluded
that
the
2
nd
Le
v
el
of
WBCH
ca
n
be
applied
to
CBIR
system.
K
e
yw
or
ds:
CBIR,
W
a
v
elet,
Color
Histog
r
am
Cop
yright
c
2018
Univer
sitas
Ahmad
Dahlan.
All
rights
reser
ved.
1.
Intr
oduction
In
this
er
a,
the
large
n
umber
of
digital
images
ha
v
e
increased
r
apidly
.
This
is
because
the
large
n
umber
of
images
data
from
v
ar
ious
domains
,
such
as
f
ashion,
biometr
ic
,
g
r
aphic
design,
architecture
,
etc.
are
in
demand.
One
of
the
techniques
f
or
digital
image
processing
is
Content
Based
Image
Retr
ie
v
al
(CBIR).
CBIR
has
been
an
activ
e
research
area
that
helps
to
access
and
find
the
images
from
huge
image
database
since
1990
[1].
The
main
idea
of
CBIR
system
is
to
e
xtr
act
the
lo
w-le
v
el
f
eatures
which
are
used
to
measure
similar
ity
[2].
It
applies
the
computer
vision
techniques
in
image
retr
ie
v
al
based
on
lo
w-le
v
el
f
eatures
which
can
be
automatically
der
iv
ed
from
the
f
eatures
presented
in
the
images
,
such
as
color
,
te
xture
,
or
shapes
[3].
The
gener
al
systems
in
CBIR
usually
only
use
the
lo
w-le
v
el
f
eatures
,
such
as
color
,
te
xture
,
and
shape
,
and
it
doesn’t
include
an
y
semantic
le
v
el.
Color
and
T
e
xture
are
the
tw
o
most
common
f
eatures
used
in
CBIR.
The
color
histog
r
am
is
the
first
technique
introduced
in
pix
el
domain
[4].
It
is
commonly
used
in
image
compar
ison
because
it
is
simple
to
compute
and
rob
ust
against
small
changes
in
camer
a
vie
wpoint
[5].
The
te
xtur
e
is
also
claimed
to
be
the
essential
f
eature
in
image
retr
ie
v
al
because
it
can
be
decomposed
into
se
v
er
al
par
ameters
,
such
as
coarseness
,
contr
ast,
and
d
irectionality
[6].
Thus
,
man
y
researches
ha
v
e
used
color
and
te
xture
f
eatures
in
b
uilding
the
CBIR
system.
Y
ouness
et
al.
[7]
proposed
a
no
v
el
method
f
or
retr
ie
v
al
system
using
Gabor
filters
and
2-D
ESPRIT
method.
In
this
study
,
each
image
is
char
acter
iz
ed
b
y
the
pair
giv
en
using
Gabor
filters
and
the
2-D
ESPRIT
method
applied
to
the
or
iginal
image
.
This
e
xper
iment
achie
v
es
a
v
er
age
precision
of
80.19%
using
Brodatz
images
database
.
Ir
ianto
[8]
used
the
Region
Gro
wing
Segmentation
f
or
searching
and
retr
ie
v
e
image
from
the
database
.
Compared
to
Discrete
Cosine
T
r
ansf
or
m
(DCT)
images
,
this
study
can
gain
more
efficient
time
and
simplify
the
algor
ithm.
Lin
et
a
l.
[9]
introduced
three
image
f
eatures
which
are:
color
,
te
xture
,
and
color
distr
ib
ution
in
order
to
de
v
elop
a
smar
t
retr
ie
v
al
system.
This
e
xper
iment
calculates
Diff
erence
betw
een
Pix
els
of
Scan
P
atter
n
(DBPSP),
Color
Histog
r
am
f
or
K-mean
(CHKM)
and
Color
Receiv
ed
December
23,
2016;
Re
vised
F
ebr
uar
y
5,
2018
;
Accepted
F
ebr
uar
y
23,
2018
DOI
:
10.12928/TELKOMNIKA.v16i3.7771
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
1693-6930
1257
Co-occurrence
Matr
ix
(CCM)
respectiv
ely
and
enhance
the
perf
or
mance
accur
acy
and
simplified
the
image
retr
ie
v
al
process
.
Ragupathi
et
al.
[10]
proposed
a
rob
ust
image
retr
ie
v
al
system
using
the
combination
of
diff
erent
f
eature
e
xtr
action
methods
,
such
as
Color
Histog
r
am
(CH),
Gabor
T
r
ansf
or
m
(GT),
the
combination
of
CH
and
GT
,
Cont
our
let
T
r
ansf
or
m
and
the
combination
of
CH
and
Contour
let
T
r
ansf
or
m.
Hiremath
and
Pujar
i
[11]
ha
v
e
used
the
combination
of
color
,
te
xture
and
shape
f
eatures
within
a
m
ultiresolution
m
ultig
r
id
fr
ame
w
or
k.
The
research
pro
vides
a
rob
ust
f
eature
set
and
achiv
e
the
highest
precision
compared
to
other
retr
ie
v
al
systems
.
Another
research
comes
from
Manimala
and
Hemachandr
an
[12].
The
y
introduced
the
W
a
v
elet
Based
Color
Histog
r
am
(WBCH)
method
in
image
retr
ie
v
al
which
combines
the
HSV
color
and
Gabor
te
xture
f
eatures
of
the
image
.
The
study
giv
es
a
promising
result
which
pro
v
ed
that
WBCH
has
be
tter
an
a
v
er
age
precision
compared
to
the
other
fiv
e
methods
(0.762).
But
this
method
only
limited
to
the
first
le
v
el
of
WBCH.
This
paper
attempts
to
impro
ving
the
a
v
er
age
precision
of
the
retr
ie
v
al
system
b
y
changing
the
w
a
v
elet
le
v
el
from
the
first
le
v
el
to
the
second
le
v
el
and
third
le
v
el
of
the
w
a
v
elet
in
order
to
obtain
more
precision.
2.
Resear
c
h
Method
2.1.
Materials
Data
set
used
in
this
study
is
W
ang’
s
image
database
which
is
also
one
of
the
standard
databases
f
or
CBIR
that
contains
1000
images
from
the
Corel
image
database
represented
with
RGB
color
space
.
The
images
w
ere
divided
into
10
categor
ies
which
are
Afr
ican
P
eople
,
Beach,
Buildings
,
Buses
,
Dinosaurs
,
Elephants
,
Flo
w
ers
,
Horses
,
Mountains
,
and
F
ood
with
JPEG
f
or
mat
and
usually
used
in
a
gener
al
pur
pose
image
database
f
or
e
xper
imentation.
2.2.
Methods
Basically
,
there
are
tw
o
steps
f
or
compar
ing
each
image
in
the
database
and
quer
y
image
,
which
are:
F
eature
Extr
action
and
Similar
ity
Matching.
F
or
the
f
eature
e
xtr
action
step
,
it
is
used
to
e
xtr
act
the
images
f
eatures
f
or
classifying
the
objects
.
Similar
ity
Matching
is
used
to
get
a
result
that
is
visually
similar
[13].
F
eature
that
used
in
this
study
are
color
and
te
xture
,
while
f
or
similar
ity
matching
using
Histog
r
am
Intersection.
Based
on
the
Figure
1,
t
he
proposed
method
will
be
applied
to
each
database
images
and
the
quer
y
images
.
Firstly
,
e
v
er
y
f
eature
in
each
image
will
be
e
xtr
acted
first
and
after
that,
the
resemb
lance
to
the
quer
y
image
and
the
image
in
the
database
will
be
obtained.
Here
are
se
v
er
al
steps
in
f
eature
e
xtr
action
phase:
1.
Image
Decomposition
using
Haar
W
a
v
elet
In
the
first
step
,
all
Red,
Green,
and
Blue
component
in
database
and
quer
y
images
are
decomposed
using
2
nd
le
v
el
Haar
W
a
v
elet.
The
results
of
this
step
are:
appro
ximate
coefficient
and
v
er
tical,
hor
iz
ontal
and
diagonal
detail
coefficients
.
After
that,
the
appro
ximate
coefficient,
hor
iz
ontal,
and
v
er
tical
coefficient
of
Red,
Green,
and
Blue
components
are
combined.
The
combined
appro
ximate
coefficient
assign
with
0.01,
hor
iz
ontal
with
0.008,
and
v
er
tical
with
0.008
(e
xper
imentally
obser
v
ed
v
alues).
2.
Con
v
er
t
(LL,
LH,
and
HL)
of
RGB
to
HSV
The
frequency
sub
bands
which
get
from
image
decomposition
steps
(appro
ximate
(LL),
hor
iz
ontal
(LH),
and
v
er
tical
coefficients
(HL)
where
L
denotes
lo
w
frequency
and
H
denotes
high
frequency)
are
con
v
er
ted
into
HSV
plane
in
order
to
e
xtr
act
the
color
f
eature
.
3.
Quantiz
e
HSV
to
(8,8,8)
F
or
reducing
the
n
umber
o
f
colors
,
the
color
is
quant
iz
e
d
using
HSV
color
histog
r
am
b
y
assigning
8
le
v
el
each
to
Hue
,
Satur
ation,
and
V
alue
components
.
So
,
the
quantization
will
giv
e
HSV
with
512
histog
r
am
bins
(8
x
8
x
8).
4.
Compute
the
histog
r
am
The
last
step
is
computing
the
nor
maliz
ed
histog
r
am
b
y
dividing
with
the
total
n
umber
of
pix
els
.
W
a
v
elet-Based
Color
Histog
r
am
on
Content-Based
Image
Retr
ie
v
al
(Ale
xander)
Evaluation Warning : The document was created with Spire.PDF for Python.
1258
ISSN:
1693-6930
Figure
1.
Flo
w
Diag
r
am
of
the
Proposed
Method
After
f
eature
e
xtr
action
phase
has
been
completed,
the
ne
xt
step
is
similar
ity
matching.
The
steps
of
similar
ity
matching
consist
of:
1.
Similar
ity
computation
with
Distance
Function
After
e
xtr
acting
the
f
eatures
of
quer
y
image
,
the
ne
xt
step
to
be
tak
en
is
computing
the
similar
ity
f
eature
of
quer
y
image
and
all
images
in
the
database
.
The
calculation
is
perf
or
med
b
y
using
histog
r
am
intersection
distance
using
the
equation
1.
Where
|Q|
represents
the
magnitude
of
the
histog
r
am
f
or
quer
y
image
and
|D|
represents
the
magnitude
of
the
histog
r
am
representativ
e
image
in
database
.
d
I
D
=
P
n
i
=1
min
[
Q
[
i
]
;
D
[
i
]]
min
[
j
Q
[
i
]
j
;
D
[
i
]]
(1)
2.
Retr
ie
v
ed
Images
The
10
most
rele
v
ant
images
(with
most
similar
histog
r
am)
are
sho
wn
as
the
result
of
retr
ie
v
al.
3.
Result
and
Anal
ysis
The
e
xper
iment
sho
ws
that
WBCH
using
2
nd
le
v
el
w
a
v
elet
giv
es
more
precision
than
the
others
,
including
WBCH
using
the
1
st
and
3
rd
le
v
el
w
a
v
elet.
The
2
nd
le
v
el
WBCH
impro
v
es
the
a
v
er
age
precision
of
CBIR
system
f
or
0.010.
The
compar
ison
of
precision
result
betw
een
2
nd
le
v
el
WBCH
and
the
other
methods
is
sho
wn
on
T
ab
le
1
(W
a
v
elet
Based
Color
Histog
r
am
/
WBCH;
Color
Histog
r
am
/CH;
Color-T
e
xture
and
Color-Histog
r
am
based
Image
Retr
ie
v
al
System
/
CTCHIRS;
Color
and
T
e
xture
F
eatures
f
or
Content
Based
Image
Retr
ie
v
al
/
CTIRS;
The
combination
of
color
,
te
xture
and
shape
f
eatures
using
image
and
its
complement
/
CTSIRS;
Content
based
Image
Retr
ie
v
al
System
based
on
Dominant
Color
and
T
e
xture
F
eatures
/
CTDCIRS).
T
ab
le
1
sho
ws
the
precision
v
alue
of
each
categor
y
of
the
image
and
also
the
a
v
er
age
precision,
while
the
sample
of
retr
ie
v
ed
images
of
e
v
er
y
categor
y
is
sho
wn
on
T
ab
le
2.
The
compar
ison
of
precision
and
recall
betw
een
2
nd
and
3
rd
le
v
el
WBCH
are
sho
wn
in
figure
2,
3,
4,
5,
6,
7,
8,
9,
10,
and
11.
TELK
OMNIKA
V
ol.
16,
No
.
3,
J
une
2018
:
1256
1263
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
1693-6930
1259
T
ab
le
1.
Precision
Result
using
Diff
erent
Methods
Classes
Categor
y
3
r
d
Le
vel
WBCH
2
nd
Le
vel
WBCH
1
st
Le
vel
WBCH
[12]
CH
[12]
CTC-
HIRS
[9]
CTI-
RS
[10]
CTS-
IRS
[11]
CTD-
CIRS
[14]
1
Afr
ican
P
eople
0.836
0.856
0.650
0.720
0.680
0.750
0.540
0.562
2
Beach
0.441
0.468
0.620
0.530
0.540
0.600
0.380
0.536
3
Buildings
0.642
0.729
0.710
0.610
0.560
0.430
0.300
0.610
4
Buses
0.859
0.851
0.920
0.930
0.890
0.690
0.640
0.893
5
Dinosaurs
0.996
0.997
0.970
0.950
0.990
1.000
0.960
0.984
6
Elephants
0.678
0.723
0.860
0.840
0.660
0.720
0.620
0.578
7
Flo
w
ers
0.922
0.911
0.760
0.660
0.890
0.930
0.680
0.899
8
Horses
0.776
0.799
0.870
0.890
0.800
0.910
0.750
0.780
9
Mountains
0.958
0.946
0.490
0.470
0.520
0.360
0.450
0.512
10
F
ood
0.462
0.485
0.770
0.820
0.730
0.650
0.530
0.694
A
v
er
age
Precision
0.757
0.777
0.762
0.742
0.726
0.704
0.585
0.705
T
ab
le
2.
Sample
Image
Retr
ie
v
al
Results
using
2
nd
le
v
el
WBCH
Categor
y
Quer
y
Retrie
ved
Ima
g
es
Afr
ican
P
eople
Beach
Buildings
W
a
v
elet-Based
Color
Histog
r
am
on
Content-Based
Image
Retr
ie
v
al
(Ale
xander)
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1260
ISSN:
1693-6930
Categor
y
Quer
y
Retrie
ved
Ima
g
es
Buses
Dinosaurs
Elephants
Flo
w
ers
Horses
TELK
OMNIKA
V
ol.
16,
No
.
3,
J
une
2018
:
1256
1263
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
1693-6930
1261
Categor
y
Quer
y
Retrie
ved
Ima
g
es
Mountains
F
ood
Based
on
the
precision
and
recall
in
figure
2-11,
the
w
a
v
elet
le
v
el
2
giv
es
slightly
better
precision
than
w
a
v
elet
le
v
el
3.
As
can
be
seen
on
se
v
er
al
categor
ies
,
such
as
Afr
ican
P
eople
(Figure
2),
Beach
(Figure
3),
Buses
(Figure
5),
Dinosaurs
(Figure
6),
Flo
w
ers
(Figure
8),
Mountains
(Figure
10)
and
F
oods
(Figure
11),
there
are
no
significant
diff
erence
betw
een
precision
using
w
a
v
elet
le
v
el
2
and
w
a
v
elet
le
v
el
3.
While
the
significant
changes
are
highly
visib
le
on
the
categor
ies
of
Buildings
(Figure
4),
Elephants
(Figure
7)
and
Horses
(Figure
9).
This
e
xper
iment
pro
v
es
that
w
a
v
elet
le
v
el
2
is
mostly
super
ior
than
w
a
v
elet
le
v
el
3.
4.
Conc
lusion
Based
on
the
e
xper
iment
conducted,
it
can
be
concluded
that
2
nd
le
v
el
W
a
v
elet
Based
Color
Histog
r
am
(2
nd
le
v
el
WBCH)
is
a
better
CBIR
method
compared
to
1
st
le
v
el
WBCH,
w
a
v
elet
te
xture
,
color
histog
r
am,
and
color
co-occurrence
matr
ix.
The
a
v
er
age
precision
of
2
nd
le
v
el
WBCH
is
0.777,
which
impro
v
es
the
a
v
er
age
precision
of
1
st
le
v
el
WBCH
f
or
0.010.
The
2
nd
le
v
el
WBCH
is
also
sur
passing
the
a
v
er
age
precision
of
the
3
rd
le
v
el
WBCH.
Since
2
nd
le
v
el
Figure
2.
Precision
and
Recall
f
or
Afr
ican
P
eople
Figure
3.
Precision
and
Recall
f
or
Beach
W
a
v
elet-Based
Color
Histog
r
am
on
Content-Based
Image
Retr
ie
v
al
(Ale
xander)
Evaluation Warning : The document was created with Spire.PDF for Python.
1262
ISSN:
1693-6930
Figure
4.
Precision
and
Recall
f
or
Buildings
Figure
5.
Precision
and
Recall
f
or
Buses
Figure
6.
Precision
and
Recall
f
or
Dinosaurs
Figure
7.
Precision
and
Recall
f
or
Elephants
Figure
8.
Precision
and
Recall
f
or
Flo
w
ers
Figure
9.
Precision
and
Recall
f
or
Horses
Figure
10.
Precision
and
Recall
f
or
Mountains
Figure
11.
Precision
and
Recall
f
or
F
ood
TELK
OMNIKA
V
ol.
16,
No
.
3,
J
une
2018
:
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TELK
OMNIKA
ISSN:
1693-6930
1263
WBCH
obtain
promising
result,
w
e
kno
w
that
this
method
can
be
applied
in
a
CBIR
system
f
or
man
y
domains
.
F
or
future
w
or
k,
in
order
to
impro
v
e
the
precision
of
image
retr
ie
v
al,
shape
can
be
included
as
the
f
eature
to
be
e
v
aluated.
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a
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am
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Retr
ie
v
al
(Ale
xander)
Evaluation Warning : The document was created with Spire.PDF for Python.