International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
Colour Based Image Searching & Retrieving System
Yshalika, M.Tech (C.S.) UPTU
Asst. Prof. Brijesh Pandey (C.S.) UPTU
GITM, LUCKNOW, (U.P.) INDIA
GITM, LUCKNOW, (U.P.) INDIA
Abstract- In the recent years, content based image
extraction has been one of the most active areas of
research. Varied indexing techniques have been used that
are based on the distribution of universal features.
There is limitation to these universal feature distributions
as it cannot capture the information pertaining to the
local image. In this paper we propose a content based
image extraction procedure that makes use of the colour
and textures. For the purpose of enhancing the power of
colour indexing procedures we make use of the minimum
amount of spatial data in the colour indexing. An image
is sub-divided horizontally in three equal nonoverlapping areas. In every area we shall extract the
initial three moments of the colour from the colour
channel and save them in the index. In a HSV colour we
save 27 floating point numbers for each image. For the
texture feature Gabor texture features are utilized.
Weights are assigned to each and every feature and then
similarity is calculated by combining the features of
colour and texture by making use of the Canberra
distance. Experimental outcomes reveal that the
proposed method has high extraction accuracy.
Keywords: Colour moments, Gabor wavelet, CBIR,
Canberra distance.
1. INRODUCTION
CBIR has become a very prominent topic of
research due to the proliferation of the image and
video data in digital form. Increment in the
availability of the bandwidth provides increased
access to the internet thereby allowing the users to
browse and search for image and video database.
Thus, quick extraction of the image from a large
database is an issues that needs to be addressed.
The desired features of CBIR include high
extraction efficiency and lower complexity in
computation. In the traditional databases of image
the extraction is done through the use of keyword
searching. There are several disadvantages of using
keyword for the extraction of image such as there is
no fixed set of words illustrating the contents of the
image, annotation of keyword is subjective. An
alternative to these disadvantages could a CBIR in
which indexing of the images would be done by
their texture, shape and colour, etc. The required
SSN: 2231-5381
image is extracted from the large database based on
the features that can be extracted automatically.
Most of the CBIR systems work in a similar
manner. An extraction of the feature vector is done
from the every image of the database and the
feature vectors are placed in the form of database
index. During the period of querying an extraction
of the feature vector takes place from the query
image. The essential difference in the varied
systems lies in the extracted features along with the
algorithms that are utilized for the comparing the
feature vectors. The systems are widely using
features such as texture, colour, spatial data that are
available in a variety of forms. CBIR is gaining
prominence due to the increase in the demand of
the searching image database. In image extraction
the colour features that are mostly used are visual
features since extraction is easier in comparison to
the shape information and texture.
Swain and Ballard’s work on colour indexing
which is based on the colour histograms illustrates
the capacity of using colour for the purpose of
indexing. Striker and Oren go [12] have
demonstrated that minute based colour circulation
components can be coordinated all the more
vigorously than colour histograms as histograms
don't catch spatial relationship of colour locales and
in this way, they have constrained segregating
force. The framework introduced in [13]
coordinates for the most part the calculations
brought in [11] into a database environment.
Sensible results can be accomplished utilizing the
aforementioned calculations, yet it is clear that the
false positives which are recovered result from the
need of spatial data in the list. The easiest approach
to store spatial data in the list is to partition the
picture into sub-pictures and afterward remove the
colour components for each sub-picture. Colour
correlogram and colour cognizance vector can
consolidate the spatial relationship of colour areas
too as the worldwide conveyance of nearby spatial
connection of hues. These strategies perform
superior to anything customary colour histograms
when utilized for substance based picture recovery.
http://www.ijettjournal.org
Page 115
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
Sensible results can be accomplished utilizing the
aforementioned calculations, yet it is clear that the
false positives which are recovered result from the
need of spatial data in the file. The least difficult
approach to store spatial data in the list is to
separate the picture into sub-pictures and afterward
extricate the colour elements for each sub-picture.
Colour correlogram and colour intelligibility vector
can consolidate the spatial relationship of colour
districts also as the worldwide dispersion of
neighbourhood spatial relationship of hues. These
procedures perform superior to anything
conventional colour histograms when utilized for
substance based picture recovery. Surface is a vital
component of normal pictures. An assortment of
methods has been produced for measuring surface
comparability. Most systems depend on looking at
estimations of what are known as second-request
measurements ascertained from inquiry and put
away pictures [14]. These systems ascertain
measures of picture composition, for example, the
level of complexity, coarseness, directionality and
normality [15 and 16]; or periodicity, directionality
and haphazardness [17]. Elective routines for
surface investigation for picture recovery
incorporate the utilization of Gabor channels [3]
and fractals [18].
Gabor channel (or Gabor wavelet) is generally
received to separate surface components from the
pictures for picture recovery [3, 19, 20, 21, 22, 23],
and has been appeared to be extremely proficient.
Manjunath and Ma [3] have demonstrated that
picture recovery utilizing Gabor components beats
that utilizing Pyramid-organized wavelet change
(PWT)
highlights,
tree-organized
wavelet
transform (TWT) highlights and multi resolution
synchronous autoregressive model (MRSAR)
highlights. Henceforth, in our proposed technique,
Gabor channel is utilized for extraction of
composition components.
An image recovery system in view of the primitives
of colour moment is proposed in [24]. In the wake
of partitioning a picture into a few hinders, the
colour snippets of all pieces are removed and
grouped into a few classes in view of a quick noniterative bunching calculation. The mean vector of
every class is considered as a primitive of the
picture what not primitives are utilized as highlight
vectors. Two test databases from Corel were
utilized and looked at the exhibitions of the
proposed system with other existing ones. The trial
SSN: 2231-5381
results demonstrated that the proposed system is
generally superior to anything others.
Choras et al. [25] proposed an incorporated colour,
surface and shape highlight extraction system in
which Gabor filtration is utilized for deciding the
quantity of areas of hobby (returns for capital
invested). They computed surface and colour
components from the returns on initial capital
investment in light of edge Gabor components and
histograms, colour moment in YUV space, and
shape components in light of Zernike moment. The
components introduced turned out to be proficient
in deciding likeness between pictures.
Xue and Wanjun [26] proposed a system in which
the highlight extraction systems for colour
histograms and colour moment are coordinated to
remove the colour highlight. They reported that the
review and accuracy had been moved forward
what's more, the record sorting was better.
A technique taking into account colour and
composition components is proposed in [27]. As
colour highlight, they utilize colour snippets of the
Tone, Saturation and Value (HSV) of the picture
and Gabor descriptors are received as composition
components. They doled out weights to every
component and computed the similitude with
joined elements of colour and surface utilizing
standardized Euclidean separation. They reported
that the proposed strategy has higher recovery
precision than the ordinary system utilizing colour
and surface components. The element measurement
is additionally lower than the ordinary techniques.
It ordinarily requires muddled division of the object
from the foundation when clients determine the
inquiry "substance" or "items" of their advantage
and just wish to recover pictures containing
applicable items, while disregarding unimportant
picture regions, (for example, the foundation ).
Kumar et al. [28] proposed a model in which the
client can choose "object of client's enthusiasm" of
diverse
shapes,
non-homogenous
surface
containing diverse hues, in any case of numerous
articles present in the same picture utilizing
changed devices like polygonal, rectangle, circle
selector devices. A two state system is utilized to
inquiry the picture from the picture database. To
start with, they incorporate worldwide colour and
composition highlight vectors to tight down the
inquiry space and in the next state they process
http://www.ijettjournal.org
Page 116
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
utilizing neighbourhood highlights. As colour and
surface elements, they utilized colour moment and
sub-band insights of wavelet decay. They reported
that objects with non-uniform colour and nonhomogenous locales can be discovered adequately.
A system in view of the primitives of colour
moments is proposed in [29]. In the system, a
picture is partitioned into four portions and the
colour moment removed from the portions is
bunched into four classes. They consider the mean
snippets of every class as a primitive of the picture.
All primitives are utilized as components and every
class mean is consolidated into a solitary class
mean. The separation between inquiry picture mean
with the comparing database pictures are computed
by utilizing Sum-of-Absolute-Differences (Pitiful).
They reported that the proposed strategy in light of
colour moments shows preferable execution over
the nearby histogram system.
A multi highlight model for the Content Based
Image Recovery System is proposed by joining the
Colour Histogram, colour Moment, surface, and
edge Histogram descriptor highlights. Clients were
offered choices to select the fitting element
extraction strategy for best results. They report the
outcomes are entirely useful for a large portion of
the question pictures and it is conceivable to further
enhance by tweaking the limit and including
significance criticism.
worldwide colour indexing systems, we encode
spatial data in the file by isolating every picture in
the database into three equivalent non-overlapping
even locales as appeared in Fig. 2(e). Tentatively it
is found that out of the different picture divisions,
the recovery dividing so as to take into account
colour minute the picture as appeared in Fig. 2(e)
gives the best recovery execution. Every district is
spoken to by a vector which comprises of a sum of
9 qualities i.e., normal tint, change of shade and
Skegness of tint, normal immersion, change of
immersion and Skegness of immersion, normal of
worth, difference of quality and Skegness of worth
from each of the district. Hence every picture in the
database is put away as a vector of 27 coasting
point qualities (or 3 areas, each district being
spoken to by a vector of 9 skimming point values).
The trial results demonstrate that the occasions
removed from the picture partitioned on a level
plane into 3 non-overlapping meet areas gives the
best execution and thus in our proposed system
colour moment are removed from these 3 flat areas.
The rest of the paper is composed as takes after: in
area 2, colour highlight extraction and closeness
estimations are displayed. In Section 3, surface
component extraction and surface closeness
estimation are displayed. Area 4 traces the
proposed strategy. Area 5 depicts the examinations
and results. At long last, conclusions are introduced
in segment 6.
Maheshwari et al. [31] have proposed a technique
in which Colour minute and Gabor channel are
utilized to concentrate highlights for picture
dataset. K-means and progressive grouping
calculation is connected to amass the picture
dataset into different groups. Two routines for
substance based picture recovery utilizing colour
what's more, composition components have been
actualized in [32]. In both the routines, highlight
extraction technique is done utilizing colour minute
while highlight extraction of surface is finished
utilizing wavelet surface components and Gabor
composition highlights. Top pictures are recovered
utilizing Euclidean separation and Chi-square
separation and they have made relative
examination.
2. Colour Representation
Colour is a standout amongst the most critical
elements
that
make
conceivable
the
acknowledgment of pictures by people and colour
highlight is a standout amongst the most generally
utilized visual components as a part of picture
recovery. Colour is a property that relies on upon
the impression of light to the eye and the preparing
of that data in the cerebrum. It is an essential
measurement of human visual discernment that
permits separation and acknowledgment of visual
data [33]. Colour components are moderately
simple to concentrate and coordinate, and have
been found to be powerful to index and looking of
colour pictures in picture databases.
We trust that an insignificant measure of spatial
data encoded in the colour file will enhance the
segregating force of plain colour indexing systems.
In this paper, in request to settle the impediments of
One of the primary parts of colour highlight
extraction is the decision of a colour space. A
colour space is a multidimensional space in which
the distinctive measurements speak to the
distinctive segments of colour. An illustration of a
SSN: 2231-5381
http://www.ijettjournal.org
Page 117
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
colour space is RGB, which doles out to every
pixel a three component vector giving the colour
intensities of the three essential hues, red, green
and blue. The space traversed by the R, G, and B
values totally depicts obvious hues, which are
spoken to as vectors in the 3D RGB colour space.
Subsequently, the RGB colour space gives a
helpful beginning stage for speaking to colour
elements of pictures. On the other hand, the RGB
colour space is not perceptually uniform. All the
more particularly, equivalent separations in diverse
force ranges and along distinctive measurements of
the 3D RGB colour space don't compare to
equivalent view of colour difference.
The RGB colour space can be changed to produce
other colour spaces. The thought for colour space
change is to add to a model of colour space that is
perceptually comparative with human colour
vision. Colour spaces, for example, HSV, CIE 1976
(LAB), and CIE 1976 (LUV) are created by
nonlinear change of the RGB space. The CIE
colour spaces speak to the three attributes that best
portray colour perceptually: tint, softness, and
immersion. Be that as it may, the CIE colour spaces
are badly designed in light of the estimation
complexities of the change to and from the RGB
colour space. HSV colour space is additionally a
nonlinear change of the RGB, be that as it may, it is
effectively invertible [33]. The HSV colour space is
roughly perceptually uniform. In this paper, we
utilize HSV colour space to concentrate colour
elements.
The HSV colour space is generally utilized as a
part of the field of colour vision. The chromatic
segments shade, immersion and quality compare
intimately with the classes of human colour
discernment. The HSV estimations of a pixel can
be changed from its RGB representation as
indicated by the taking after equation:
The objective of colour indexing is to recover every
one of the pictures whose colour arrangements are
like the colour arrangement of the question picture.
Histograms are helpful since they are generally
uncaring to position and introduction changes and
they are adequately precise [11]. Be that as it may,
they don't catch spatial relationship of colour
districts and subsequently, they have restricted
segregating
force.
Numerous
productions
concentrate on colour indexing methods taking into
account worldwide colour circulations. These
worldwide
disseminations
have
restricted
separating capacity in light of the fact that they
can't catch neighbourhood colour data. Colour
correlogram and colour intelligibility vector can
join the spatial relationship of colour locales and
also the worldwide circulation of neighbourhood
spatial connection of hues. These strategies
perform superior to anything customary colour
histograms at the point when utilized for substance
based picture recovery. Be that as it may, they
require exceptionally costly calculation. Colour
moment has been effectively utilized as a part of
substance based picture recovery frameworks. It
has been demonstrated [12] that describing one
dimensional colour dispersions with the initial three
moment is more powerful and runs quicker than the
histogram based systems.
In this paper, so as to enhance the separating force
of colour indexing methods, we isolate the picture
on a level plane into three equivalent non-covering
locales and from each of the three districts, we
separate from every colour channel the initial three
snippets of the colour circulation what's more, store
the 27 coasting point numbers in the file of the
picture. In the event that we translate the colour
conveyance of a picture as a likelihood
appropriation, then the colour dissemination can be
portrayed by its moment [12]. On the off chance
that the estimation of the it colour channel at the jth
picture pixel is Iij and the quantity of pixels is N,
then the record passages identified with this colour
channel and the area "r" are:
2.1. Colour Feature Extraction
SSN: 2231-5381
http://www.ijettjournal.org
Page 118
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
Canberra separation measure is utilized for
similitude
correlation.
It
permits
The passages Er,i (1 <= i <=3) are the normal shade
of the locale r. The passages
and sr,i are the
fluctuation and the skewness of every colour divert
in this locale 'r'.
The record passage for one picture comprises of
Record size = number of locales X number of
colour
us the list of capabilities to be in normalized
structure. The Canberra separation measure is
given by:
Channels X 3 gliding point numbers.
For our situation, the picture is partitioned on a
level plane into three meet locales and we need to
store 27 floating point numbers per picture.
Along these lines, the element vector fc of length 27
is given by:
(1<=r, i<=3) r refers to the locale and i refers to the
colour channel.
Where x and y are the component vectors of
inquiry and database picture separately, of
measurement d. The accompanying calculation is
proposed to decide the closeness between inquiry
picture and a picture in the picture database:
2.2 Colour similarity measure
Step1. Information inquiry picture I
To decide the comparability of two pictures at
inquiry time, we measure the comparability
between their records. Let H and I be two colour
pictures with c colour channels. In the event that
the record sections of these pictures for their areas
are
resp. Fr2,I,
and sr2,I resp. tr2,i
,
Step2. Change over RGB colour space picture into
HSV colour space.
Step 3.Partition the picture into three equivalent
non-covering flat districts
Step4. Compute the minutes r2,i F , r2,i
, r2,i t (segment 2.1) for every colour channel of
every area to get 27 numbers from three areas of
the inquiry picture I.
Step5. Apply Step 2 to Step 4 to the picture Hj in
the database, to compute the minutes r1,i E , r1,i
, and r1,i s
from the areas of Hj to get 27 numbers.
Step6. Compute the Distance dj(Hj,I) between the
two pictures utilizing Eq.(5) and store in an cluster
d.
SSN: 2231-5381
http://www.ijettjournal.org
Page 119
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
Step 7. Increase j, rehash step 5 and 6 for every one
of the pictures in the database.
Step 8. The cluster d is sorted in climbing request.
The picture comparing to the first component of d
is the most comparative picture contrasted and the
question picture I. The initial 10 best most
comparative pictures are at that point showed.
3. Composition Representation
Composition [34] is characterized as structure of
surfaces shaped by rehashing a specific component
or a few components in distinctive relative spatial
positions. By and large, the reiteration includes
nearby varieties of scale, introduction, or other
geometric and optical elements of the components.
Picture compositions are characterized as pictures
of
characteristic
textured
surfaces
also,
misleadingly made visual examples. It contains
critical data about the basic plan of the surface i.e.,
mists, leaves, blocks, fabric, and so on. It too
portrays the relationship of the surface to the
encompassing environment. It is an element that
portrays the unmistakable physical arrangement of
a surface. Gabor wavelet is broadly embraced to
concentrate surface from the pictures for recovery
and has been appeared to be exceptionally
productive. Fundamentally Gabor channels are a
gathering of wavelets, with every wavelet catching
vitality at a particular recurrence what's more,
particular introduction. The scale and introduction
tuneable property of Gabor channel makes it
particularly helpful for composition investigation.
The outline of Gabor channel is done as takes after:
[35 and 36]
For a given picture I(x,y) with size PXQ, its
discrete Gabor wavelet change is given by a
convolution:
SSN: 2231-5381
The variables of the above equation are defined as
In our execution, we utilized the accompanying
constants as ordinarily utilized as a part of the
writing: Ul =0.05, Uh =0.4, s and t range from 0 to
60, i.e., channel veil size is 60x60.
3.1 Texture Feature Extraction
Subsequent to applying Gabor channels on the
picture with distinctive introduction at diverse
scale, we get a variety of extents:
These extents speak to the vitality content at
diverse scale and introduction of the picture. The
fundamental reason for surface based recovery is to
discover pictures or areas with comparative
http://www.ijettjournal.org
Page 120
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
surface. It is expected that we are occupied with
pictures or locales that have homogenous surface;
thusly the accompanying mean μmn and standard
deviation mn of the greatness of the changed
coefficients are utilized to speak to the
homogenous surface component of the locale
A component vector g f (surface representation) is
made
utilizing
mn
μ
and
mn
as
the
element segments [3]. Four scales and 6
introductions are utilized as a part of normal
execution and the element vector of length 48 is
given by:
3.2 Texture Similarity Measure:
The surface similitude estimation of an inquiry
picture Q furthermore, an objective picture T in the
database is characterized by:
4. Proposed Method
The novel strategy for colour minute (in light of
division of the picture into 3 approach noncovering level districts) + Gabor composition
elements gives better results contrasted with the
colour minute (in light of entire picture) + Gabor
surface elements and others utilizing just single
component.
4.1 Colour Feature
In the proposed strategy, we are recovering the
pictures utilizing low level elements, colour and
composition. While recovering the pictures
utilizing colour highlight, the RGB picture is
changed over into HSV space. The worldwide
conveyances have restricted separating capacity on
the grounds that they can't catch nearby colour
data. To enhance the separating force of colour
indexing, we encode an insignificant measure of
spatial data in the list by partitioning the picture on
a level plane into three equivalent non-covering
areas and extricating moment from these areas. In
our test, we figure the colour highlight vector c f of
length 27 for question picture and pictures in the
picture database and separation between them is
figured utilizing Canberra separation measure. The
consequences of an inquiry are shown in
diminishing closeness request.
4.2. Composition Feature
On account of low level composition highlight, we
apply Gabor channels on the picture with 4 scales
and 6 introductions and we get a variety of extents.
The mean μmn and standard deviation mnof the sizes
are utilized to make a composition highlight vector
g f of length 48. Canberra separation measure is
utilized for registering the separation and the
outcomes of an inquiry are shown in diminishing
similitude request.
4.3 Image Database
For assessment of the proposed strategy, it has been
actualized utilizing Mat lab 6.5 and tried on a
general purpose WANG database [37] containing
1000 Corel pictures in JPEG arrangement of size
384 x 256 or 256 x 384. The picture set involves
100 pictures in each of 10 classifications. In our
examination, we have chosen 100 pictures
arbitrarily, containing 10 pictures in every
classification what's more, the pictures are resized
to 256 x 384. Inside of this database, it is known
whether any two pictures are of the same
classification. Specifically, a recovered picture is
SSN: 2231-5381
http://www.ijettjournal.org
Page 121
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
considered a match if and just on the off chance
that it is in the same classification as the question
4.4. Joining the Feature
The recovery result utilizing just single component
may be wasteful. It might either recover pictures
not like question picture or may neglect to recover
pictures like inquiry picture. Subsequently, to
create proficient results, we utilize blend of colour
and surface elements. The closeness in the middle
of question and target picture is measured from two
sorts of trademark elements which incorporates
colour and composition highlights. Two sorts of
attributes of pictures speak to distinctive parts of
property. Along these lines, amid comparability
measure, suitable weights are considered to
consolidate the elements. The separation between
the question picture and the picture in the database
is figured as takes after:
d =w1*d1+w2*d2 (19)
Here, w1 is the heaviness of the colour elements,
w2 is the weight of the surface components and d1
and d2 are the separations ascertained utilizing
colour minute utilizing Eq.(6) and surface
components utilizing Eq.(18). Trials demonstrate
that better recovery exhibitions are accomplished
when we set w1=0.80 and w2=0.20. The weight
element of colour highlight separation is higher
than the weight variable of composition highlight
separation in light of the fact that our database
comprises of for the most part regular pictures.
The above distance 'd' is computed between the
question picture and every one of the pictures in the
database and it is sorted in rising request. The
picture comparing to the first component of d is the
most comparative picture contrasted and the
question picture. The initial 10 beat most
comparative pictures are then shown.
Investigations and Results
The execution of a recovery framework can be
measured in terms of its accuracy and review.
Accuracy measures the capacity of the framework
to recover just models that are important, while
Recall measures the capacity of the framework to
recover all models that are important. They are
characterized as
SSN: 2231-5381
Where, |A(Ik)| refers to the quantities of pertinent
pictures in the particular classification, then the
normal accuracy for pictures having a place with
the qth classification (Aq) has been processed by
[38]:
The analysis was completed with the quantity of
recovered pictures set as 10 to process the normal
exactness P of every inquiry picture. With a
specific end goal to survey the separating force of
the strategies proposed in this paper, we completed
the tests in view of Gabor composition highlight
(GTF), colour minute - in light of entirety picture
(CMW), colour minute - picture isolated into 3
measure up to non-covering even districts (CMR),
colour moment - taking into account entire picture
+ Gabor composition highlight (CMW + GTF), and
colour minute - picture isolated into 3 rise to noncovering level areas + Gabor surface elements
(CMR + GTF). A percentage of the outcomes
utilizing the same inquiry picture of Fig. 4 (an) are
appeared in Fig. 4. From Table 1. , it is seen that
the normal accuracy (%) in view of (CMR+GTF) is
61.0 and the normal accuracy (%) in view of
(CMW+GTF) is 58.2. Along these lines the
proposed system shows unmistakably that our
http://www.ijettjournal.org
Page 122
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
encoding of spatial data in the colour record from
diverse areas of the picture essentially builds the
segregating force contrasted with the colour minute
(taking into account entire picture) + Gabor
composition elements indexing strategies in which
colour moment are separated from the whole
picture.
It is likewise seen that the estimation of the normal
precisions (%) in view of single components i.e.
just Gabor surface elements or just Colour
moments are not exactly the normal precisions (%)
of consolidated elements of colour moment and
Gabor composition highlights as appeared in Table
2. what's more, Table 3. This additionally
demonstrates that there is impressive increment in
recovery productivity when both colour and surface
elements are joined for CBIR.
5. Conclusion
In this paper, we have proposed a productive
picture recovery system taking into account colour
moment and Gabor composition highlights. To
enhance the segregating force of colour indexing
SSN: 2231-5381
http://www.ijettjournal.org
Page 123
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
procedures, we encode a negligible measure of
spatial data in the record by extricating components
from the locales of the picture isolated on a level
plane into three rise to non-covering locales. In this
methodology, from each Class Normal Precision
utilizing GTF CMW CMR CMW + GTF CMR +
GTF
Building 0.33 0.25 0.35 0.30 0.36
show that the proposed strategy has higher
recovery precision than other customary routines
consolidating colour moment and surface elements
taking into account worldwide elements approach.
The examination likewise demonstrates that just
colour highlights or just surface components are
not adequate to portray a picture. There is
impressive increment in recovery effectiveness
when both colour and surface elements are
consolidated. Along these lines it is rightly said that
just colour or just composition can't separate a
cheetah and a tiger.
Transports 0.35 0.67 0.78 0.60 0.77
6. References
African 0.37 0.75 0.74
Shoreline 0.27 0.46 0.38
Dinosaurs 0.99 0.74 0.83 0.96 0.95
Elephants 0.39 0.60 0.45 0.58 0.44
Blooms 0.75 0.42 0.61 0.71 0.69
Stallions 0.27 0.55 0.70 0.47 0.67
Sustenance 0.44 0.67 0.62 0.72 0.69
Mountain 0.20 0.43 0.36 0.41
Normal
Accuracy
(%)
43.6 55.4 59.0 58.2 61.0
GTF CMW GTF+CMW
Normal
Accuracy (%)
43.6 55.4 58.2
GTF CMR CMR+GTF
Normal Accuracy (%) 43.6 59.0 61.0 district in the
picture, we remove from every colour channel the
initial three snippets of the colour appropriation
and store the 27 drifting point numbers ( or 3 areas,
every district being spoken to by a vector of 9
skimming point numbers) of the picture in the file.
As its surface component, Gabor composition
descriptors are embraced. We compute the
similitude with consolidated components of colour
and surface utilizing Canberra separation as
comparability measure. Our Experimental results
SSN: 2231-5381
[1] R.Datta, D. Joshi, J.Li, J.Z. Wang, " Image retrieval:
ideas,influences, and trends of the new age ", ACM
ComputingSurveys 40(2), 2008, pp. 1-60.
[2] V.N. Gudivada, and V.V. Raghavan, “Content based
imageretrieval systems", IEEE Computer, Vol. 28, No. 9, 1995,
pp.18-22.
[3] B.S. Manjunath, and W.Y. Ma, "Texture Features
forbrowsing and retrieval of image data", IEEE Transactions
onPattern Analysis and Machine Intelligence, Vol. 18, No. 8,
1996, pp. 837-842.
[4] Y. Rui, T.S. Huang, M. Ortega, S. Mehrotra, "
Relevancefeedback : a power tool forinteractive content based
imageretrieval ",IEEE Circuits and Systems for Video
Technology ,Vol. 8, No. 5, 1998, pp. 644-655.
[5] D. Swets, and J. Weng, “Hierarchical discriminant
analysisfor image retrieval”, IEEE “PAMI, Vol. 21, No. 5, 1999,
pp.386-400.
[6] H. Zhang and D. Zhong, “A scheme for visual feature
basedimage retrieval”, Proc. SPIEstorage and retrieval for
imageand video databases, 1995.
[7] A.M. Smeulders, M. Worring and S. Santini, A. Gupta and
R.Jain, " Content-based image retrieval at the end of the early
years”, IEEE Trans Pattern Anal Machine Intell 22:, 2000,
pp. 1349-1380.
[8] R. Choras, “Content-based image retrieval using
color,texture, and shape information”, In. Sanfeliu, RiuzShulcloper J. (eds) Progress in pattern recognition, speech
and image analysis. Springer, Heidelberg. 2003.
[9] R. Corners, and C. Harlow, ”A theoretical comparisonof
texture algorithms”, IEEE Trans Pattern Anal
Machine Intell 2: 1980, pp. 204-222.
[10] P. Howarth, and S. Ruger, “Evaluation of texturefeatures
for content based image retrieval”, In: Enser P.
et. al. (eds) Image and video retrieval. Springer LNCS
3115: pp. 326-334.
[11] M.Z. Swain, and D.H. Ballard, “Color Indexing”, Intl. J.
ofComputer Vision 7(1): 1991, pp. 11-32.
[12] M. Stricker, and M. Orengo, “Similarity of color images”,
InSPIE Conference on Storage and Retrieval for Image and
Video Databases, volume 2420, 1995, pp. 381-392, San
Jose, USA.
[13] V.E. Ogle, and M. Stonebraker, “Chabot: Retrieval from a
relational database of images”, Computer, 1995, pp. 40-48.
[14]J.P. Eakins, and M.E. Graham, “Content-based
ImageRetrieval: A report to the JISC Technology Applications
Program”
http://www.unn.ac.uk/iidr/research/cbir/report.html.
[15] H. Tamura, S. Mori, T. Yamawaki, “Texture
featurescorresponding to visualperception”, IEEE Trans. On
Systems, Man and Cybernetics. 6(4): 1976, pp. 460-473.
[16]W. Niblacket. al., “The QBIC Project: QueryingImages by
Content Using Color, Texture and Shape”.Proc. Of the
Conference Storage and Retrieval for Image and Video
Databases, SPIE vol. 1908, 1993, pp. 173-187.
[17] F. Liu, and R.W. Picard, “Periodicity, directionality and
randomness: Wold features for image modelling and
http://www.ijettjournal.org
Page 124
International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
retrieval”, IEEE Transactions on Pattern Analysis and
Machine Intelligence 18(7): 1996, pp.722-733.
[18] L.M. Kaplan et al., “Fast texture database retrieval using
extended fractal features” in Storage and Retrieval for Imageand
Video Databases VI(I.K. Sethi and R.C. Jain eds), Proc.
SPIE 3312, 1998, pp.162-173.
[19] J.R. Smith, “Integrated Spatial and Feature Image
System:Retrieval, Analysis and Compression”, Ph.D. thesis,
Columbia University, 1997.
[20]Y. Deng, “A Region Representation for Image and
VideoRetrieval”, Ph.D. thesis, University of California, Santa
Barbara, 1999.
[21] W.Y. Ma, “Netra: A Toolbox for Navigation Large
ImageDatabases”, Ph.D. thesis, University of California, Santa
Barbara, 1997.
[22]S. Jeanin (ed.), “ISO/IEC JTCI/SC29/WG11/N3321:
MPEG-7 Visual Part Xperimentation Model Version
5.0”,Nordwijkerhout, 2000.
[23]A. Dimai, “Rotation Invariant Texture Description using
General Moment Invariants and Gabor Filters”, In Proc. ofthe
11th Scandinavian Conf. on Image Analysis.Vol I, 1999,
pp. 391-398.
[24] J.L. Shih, and L.H. Chen, “Color image retrieval based
onprimitives of color moments”, IEEE Proceedings online no.
20020614., 2002.
[25]R.S. Choras, T. Andrysiak, M. Choras, “Integrated
color,texture and shape information for content-based image
retrieval”, Pattern Anal Applic. 10: 333-343, 2007.
[26]B. Xue and L. Wanjun, “Research of Image Retrieval
BasedonColor”, IEEEInternational Forum on Computer
Science-Technology and Applications, 2009.
[27]Z.C. Huang, P.P.K. Chan, W.W.Y. Ng, D.S. Yeung,“
Content-based image retrievalusingcolor moment andGabor
texture feature”, in Poceedings of the IEEE NinthInternational
Conference on Machine Learning andCybernetics, Qingdao,
2010, pp. 719-724.
[28]D.K. Kumar, E.V. Sree, K. Suneera, P.V.Ch. Kumar,
“Content Based Image Retrieval – Extraction by objects of
user interest”, International Journal of Computer Science and
Engineering (IJCSE), Vol.3, No.3., 2011, pp. 1068-1074
SSN: 2231-5381
http://www.ijettjournal.org
Page 125