As with the advancement of multimedia technologies, users are not gratified with the conventional retrieval system techniques. So a application “Content Based Image Retrieval System” is introduced. CBIR is the application to retrieve the images or to search the digital images from the large database .The term “content” deals with the colour, shape, texture and all the information which is extracted from the image itself. This paper reviews the CBIR system which uses SVM classifier based algorithms for feature extraction phase.

1. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 116
Query
Formation
Image
Database
Visual
Content
Descrip
tion
Visual
Content
Descript
ion
Feature
Vectors
Feature
Database
Relevance
Feedback
Similarity
Comparison
Indexing &
Retrieval
Retrieval
resultsOutput
A Review of Feature Extraction Techniques for
CBIR based on SVM
Navneet Kaur1
, Sonika Jindal2
1
M.Tech, Department of Computer Science and Engineering
2
Assistant Professor, Department of Computer Science and Engineering
Shaheed Bhagat Singh College of Engineering and Technology, Ferozepur
dhahnavneet@gmail.com & sonikamanoj@gmail.com
Abstract: As with the advancement of multimedia
technologies, users are not gratified with the conventional
retrieval system techniques. So a application “Content Based
Image Retrieval System” is introduced. CBIR is the
application to retrieve the images or to search the digital
images from the large database .The term “content” deals
with the colour, shape, texture and all the information which
is extracted from the image itself. This paper reviews the
CBIR system which uses SVM classifier based algorithms for
feature extraction phase.
Index Terms— Content Based Image Retrieval , Support
Vector Machine , Feature Extraction, Relevance Feedback.
I. INTRODUCTION
1
Content Based Image retrieval is the method to retrieve
images based on various derived features such as colour,
texture and shape[1]. According to the users requirement,
this technique basically use visual contents to search
images from the large databases. Early techniques were
based on textual annotation of images rather than the visual
contents [2]. In other terms, firstly images are annotated
with text, and then search can be done using text based
method from traditional database management system. The
first and foremost retrieval approach based on combination
of textual data into each image and retrieve those images
by keywords which is the traditional database query
technique which is time consuming and too much gruelling
task. In CBIR system the images are extracted form the
database on the basis of visual contents and represented as
feature vectors. Furthermore, SVM is the classifier which
is basically for the regression and classification on the
basis of various tools and techniques. Various algorithms
are used to extract the features of images by using the
SVM classifier. Moreover learning techniques are used it
may be supervised or unsupervised learning techniques
which are based on the training and testing phases.
Firstly retrieve the feature vectors from the images
(features can be colour, texture and shape) and then keep
feature vectors into different databases for the future
purpose. The two images in the database is similar to the
query image only when the distance between two images
feature vectors is small. Figure 1 shows the various phases
of CBIR.
Figure 1: Content Based Image Retrieval
II. FEATURE EXTRACTION
Feature extraction for CBIR is the method of computing
the attributes of various digital images which can be used
to define information regarding the contents of the image.
A feature can be associated with the single attribute or
composite description of distinguished attributes. The
classification of features is general purpose or domain
dependent. The general purpose features can be designed
anywhere in the context whereas domain dependent
features are used for a specific application[6]. The
advantage for feature extraction is to detect the different
types of features which are used in images[9].There has
been huge work done on various approaches to detect the
different features among images. Feature extraction will be
solicited very customarily; therefore, it would be exact,
accurate and time efficient. The dimension of the vector
can be reduced by using the feature extraction techniques
on the basis of:
Colour
Texture
Shape
COLOR
Color is more considerable visual content for the retrieval
of images. It reveals the most broadly used feature in the
CBIR system. The preference for the selection of features
of the colour, based on results of the segmentation. For
illustration, if homogeneous colour is not provided by the
segmentation method then obviously this is not a better
choice[4]. Firstly to represent the colour images, colour
space is used. Typically, the summation of red, green and
blue gray level intensities is represented by the gray level
intensities which represent the RGB space.RGB space
mainly used for image display in colour space. This

2. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
117 NITTTR, Chandigarh EDIT-2015
includes three colours components red, green and blue also
known as ‘additive primaries’[4]. In contrast, for the
purpose of printing CMY color space s used whose color
components are cyan, magenta and yellow also known as
‘Subtractive Primaries’. Light absorption is produced color
in CMY space. Color features have various advantages:
Robustness
Effectiveness
Implementation Simplicity
Computational Simplicity
Low Storage Requirements
TEXTURE
Texture is one more another significant property of the
images. It deals with the visual patterns which have the
quality of uniformity or settlement that do not consequence
from only the presence of single intensity. For the purpose
of both computer vision and pattern recognition, different
texture representations have been explored.
Texture representation can be divided into various classes:
Structural methods:
These methods include the graphs and morphological
operators and their rules. This reveals with the action of
image primitives and presence of parallel objects. The
structural method introduces to retrieve the structural
information under the assumption of human visual
perception[4]. The main target image quality can be further
subdivided on the basis of original image that is distortion
free and another is distorted image. If reference of image is
known that is called to be full reference otherwise no
reference or it can be blind quality approach is obtained.
Moreover, another method introduces the reference image
is available partially which consist of set of extracted
features for evaluating the quality of distorted image.
Statistical methods
These methods consist of famous co-occurrence matrix,
Fourier power spectra, Shift invariant principal component
analysis (SPCA), Tamura feature, Multi-resolution filtering
technique such as Gabor and wavelet transform,
characterize the texture by statistical distribution of the
image intensity.
SHAPE
Shape representation can be subdivided into two categories
Boundary based which includes the outer boundary of the
shape only. This is completed by describing the region
which uses only the external features, such as the pixels
along with the object boundary.
Region Based is completely different from the boundary
based. This can be used the whole shape region by
explaining the internal characteristics such as the pixels
present in the region.
III.SUPPORT VECTOR MACHINE
SVM is the state of art classification method which is
introduced in 1992 by Bose, guy on and Vapnik.SVM is an
best tool for regression and classification.[16] Support
vector machine may be defined as this is the linear
function of the high dimensionality feature space which
consist of the postulate space. SVM is a most beneficial
technique for the data classification. Sometimes unsatisfied
results are obtained using the neural networks and even it
is easy to use. The classification task includes the training
and testing data which consist the same data instances.
Each sample in the training set consist the target values
and its various attributes.[17] The major goal of SVM is to
judge that the target value of various data instances in the
testing set which are given only the attributes of the data.
Classification in the SVM is the instance of supervised
learning. Now discussion of various algorithms on which
SVM is used.
Improved SVM also known as the SVM clustering.
Clustering is an unsupervised learning technique which
simply means the decomposition of objects into various
clusters and subgroups on the basis of similarity.
SVM with Gabor magnitude
Gabor filters are the combination of wavelets, where
individual wavelet which captures the energy at specific
direction and frequency. For the detection of different
orientation and frequencies, the Gabor filter banks are
designed[17]. A hybrid approach to CBIR is used, SVM is
trained and then therefore the database of images is labeled
using feedback from users which consider relevant and
non-relevant. Standard deviation of Gabor can be
calculated to obtain the Gabor feature vector[17]. Various
steps are included to apply the Gabor algorithm:
Divide the whole image into 16*16 sub-blocks.
Calculate for four different scales at eight different angles,
which will give eight different angles at one particular
scale.
Calculate standard deviation and mean, which gives the
Gabor feature vector.
Image Retrieval using SVM and SURF
Surf (Speeded up robust Features) is a local feature
detector; first introduced by Herbert Bay et al in 2006
which is magnificent by SIFT descriptor.
Basically SURF is the combination of 2D-Haar wavelet
which makes logical use of intrinsic images[18]. Using the
histogram of gradient orientation, construct the descriptor
vector of length 64[21]. Only CBIR with Surf and SVM
method does not provide the better results, so that is why,
use the CBIR with Surf ,Artificial Neural Network and
SVM gives the improved results. The combination of
modified SURF, Similarity matching algorithms and image
blending algorithm makes the prospective image system.
Load the image as an input.
Pre-process (Convert to grey scale, binary form).
Extract the features using Image Histogram.
Matching and recognition using SURF feature, SVM and
NN.
Display the results and obtained the average accuracy
SVM with Quadratic Distance Metric
For the extraction of colour features Global Colour
histogram is used. There was an issue for the analysis of
histogram: There is no information regarding the number
of bins which need to quantize (18). For the betterment of
results use the neural network for supervised and

3. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 118
unsupervised learning[17]. Basically neural network is the
interconnection between neurons. Artificial neural network
consist of the number of artificial neurons. With the help of
neural network, supervised and unsupervised learning
techniques display the good results. Multilayer perception
uses recurrent networks and feed forward neural
network(as shown in Figure 2) [18]. It is the property
which consist of non linear functions and having input
output patterns which involves the multiple inputs and
outputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Content
based image retrieval from the concept of textual based
information retrieval in 1998. Further this has become a
well liked technique in CBIR. Relevance feedback is a
managed active technique which is used to ameliorate the
success of information system. The fundamental scheme is
to use the positive and negative instances from the user to
enhance the system performance [2]. If the user accepts the
images as(positive examples) applicable to the query or
(negative examples) not applicable. Then the user gives the
response in the form of “Relevance feedback” indicates
over the extracted outcomes. Until the user is not satisfied,
the process can continue. Relevance feedback strategy
really helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in various
fields for different purposes. Some applications are as
follow:
Medical: The benefits of CBIR can consequence in the
various services that can use in biomedical information
systems. Large number of domains takes the advantage of
CBIR system[4]. Clinicians basically use similar cases for
clinical decision-making process.
Digital Libraries: The libraries support those services
which are based on CBIR system.
Crime
Cultural
Military
Entertainment
Given table depicts the survey on various techniques and
dataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSION
In this paper, fundamentals for content based image
retrieval is introduced which include the visual contents,
feature extraction, similarity/distance measures and user
interaction. The way the user communicates with the
content based image retrieval system, the size of the
databases, the features used and the speed of the retrieval
are the most important factors that judge the success of a
CBIR system. Moreover, it also reveals that the how
algorithms are used when SVM classifier is used for the
extraction of various features of images to obtain the
desired results as per the user’s requirement.
REFERENCES
International Journal of Computer Science and Mobile Computing,
International Journal of Computer Science and Mobile Computing,, pg.
769-775, 2014.
Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng
Fundamentals Of Content-Based Image Retrieval.
da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theory
and Applications, Institute of Computing, State University of Campinas,
Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.
Fundamental of Content Based Image Retrieval, International Journal of
Computer Science and Information Technologies,no.3260 – 3263,2012.
M.E. El Alami,A new matching strategy for content based image retrieval
system , , Applied Soft Computing,vol.14,pp.407-418,2014
Paper Technique
Dataset
(Accuracy)
Sultan Aljahdali
(2012)
Gabor Filter
COIL Dataset
(89.5%)
Sukhmanjeet Kaur
(2015)
SURF(Speeded Up
Robust Feature)
98%
M.E ElAlami
ANN(Feed Forward
Neural Network)
Wang dataset
(67.2%)
GUI-ZHI LI,YA-HUI
LIU,CHANG-
SHENG-ZHOU(2013)
Semi Supervised
Approach
Corel Dataset
Rajesh Singla and
Haseena B.A(2014)
Fast Fourier transform 85.5%
Xiukuan Zhao (2011)
Bearing fault Diagnosis
and Gear Fault
-
K.Ashok Kumar &
Y.V.Bhaskar
Reddy(2012)
Quadratic Distance
Metric Algorithm
Corel Image
dataset &
benchmark dataset
S. Mangijao Singh &
K.
Hemachandran(2012)
Canberraa Distance And
Gabor Wavelet
Wang dataset
Sigmoidal
Function
h(.)
h(.)
X1
1
X2
xn
1
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 118
unsupervised learning[17]. Basically neural network is the
interconnection between neurons. Artificial neural network
consist of the number of artificial neurons. With the help of
neural network, supervised and unsupervised learning
techniques display the good results. Multilayer perception
uses recurrent networks and feed forward neural
network(as shown in Figure 2) [18]. It is the property
which consist of non linear functions and having input
output patterns which involves the multiple inputs and
outputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Content
based image retrieval from the concept of textual based
information retrieval in 1998. Further this has become a
well liked technique in CBIR. Relevance feedback is a
managed active technique which is used to ameliorate the
success of information system. The fundamental scheme is
to use the positive and negative instances from the user to
enhance the system performance [2]. If the user accepts the
images as(positive examples) applicable to the query or
(negative examples) not applicable. Then the user gives the
response in the form of “Relevance feedback” indicates
over the extracted outcomes. Until the user is not satisfied,
the process can continue. Relevance feedback strategy
really helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in various
fields for different purposes. Some applications are as
follow:
Medical: The benefits of CBIR can consequence in the
various services that can use in biomedical information
systems. Large number of domains takes the advantage of
CBIR system[4]. Clinicians basically use similar cases for
clinical decision-making process.
Digital Libraries: The libraries support those services
which are based on CBIR system.
Crime
Cultural
Military
Entertainment
Given table depicts the survey on various techniques and
dataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSION
In this paper, fundamentals for content based image
retrieval is introduced which include the visual contents,
feature extraction, similarity/distance measures and user
interaction. The way the user communicates with the
content based image retrieval system, the size of the
databases, the features used and the speed of the retrieval
are the most important factors that judge the success of a
CBIR system. Moreover, it also reveals that the how
algorithms are used when SVM classifier is used for the
extraction of various features of images to obtain the
desired results as per the user’s requirement.
REFERENCES
International Journal of Computer Science and Mobile Computing,
International Journal of Computer Science and Mobile Computing,, pg.
769-775, 2014.
Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng
Fundamentals Of Content-Based Image Retrieval.
da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theory
and Applications, Institute of Computing, State University of Campinas,
Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.
Fundamental of Content Based Image Retrieval, International Journal of
Computer Science and Information Technologies,no.3260 – 3263,2012.
M.E. El Alami,A new matching strategy for content based image retrieval
system , , Applied Soft Computing,vol.14,pp.407-418,2014
Paper Technique
Dataset
(Accuracy)
Sultan Aljahdali
(2012)
Gabor Filter
COIL Dataset
(89.5%)
Sukhmanjeet Kaur
(2015)
SURF(Speeded Up
Robust Feature)
98%
M.E ElAlami
ANN(Feed Forward
Neural Network)
Wang dataset
(67.2%)
GUI-ZHI LI,YA-HUI
LIU,CHANG-
SHENG-ZHOU(2013)
Semi Supervised
Approach
Corel Dataset
Rajesh Singla and
Haseena B.A(2014)
Fast Fourier transform 85.5%
Xiukuan Zhao (2011)
Bearing fault Diagnosis
and Gear Fault
-
K.Ashok Kumar &
Y.V.Bhaskar
Reddy(2012)
Quadratic Distance
Metric Algorithm
Corel Image
dataset &
benchmark dataset
S. Mangijao Singh &
K.
Hemachandran(2012)
Canberraa Distance And
Gabor Wavelet
Wang dataset
Sigmoidal
Function
h(.)
h(.)
X1
1
X2
xn
1
Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
NITTTR, Chandigarh EDIT -2015 118
unsupervised learning[17]. Basically neural network is the
interconnection between neurons. Artificial neural network
consist of the number of artificial neurons. With the help of
neural network, supervised and unsupervised learning
techniques display the good results. Multilayer perception
uses recurrent networks and feed forward neural
network(as shown in Figure 2) [18]. It is the property
which consist of non linear functions and having input
output patterns which involves the multiple inputs and
outputs. Figure 3 shows the Simple neural network.
Figure 2: Multilayer Perceptron
Figure 3: Simple Neural Network
IV.RELEVANCE FEEDBACK
The term relevance feedback was initiated into Content
based image retrieval from the concept of textual based
information retrieval in 1998. Further this has become a
well liked technique in CBIR. Relevance feedback is a
managed active technique which is used to ameliorate the
success of information system. The fundamental scheme is
to use the positive and negative instances from the user to
enhance the system performance [2]. If the user accepts the
images as(positive examples) applicable to the query or
(negative examples) not applicable. Then the user gives the
response in the form of “Relevance feedback” indicates
over the extracted outcomes. Until the user is not satisfied,
the process can continue. Relevance feedback strategy
really helps to enhance the semantic gap problem.
V.APPLICATIONS
Content based image retrieval has been used in various
fields for different purposes. Some applications are as
follow:
Medical: The benefits of CBIR can consequence in the
various services that can use in biomedical information
systems. Large number of domains takes the advantage of
CBIR system[4]. Clinicians basically use similar cases for
clinical decision-making process.
Digital Libraries: The libraries support those services
which are based on CBIR system.
Crime
Cultural
Military
Entertainment
Given table depicts the survey on various techniques and
dataset on which SVM classifier is used.
TABLE I : REVIEW OF SOME RESEARCH PUBLICATIONS
VI. CONCLUSION
In this paper, fundamentals for content based image
retrieval is introduced which include the visual contents,
feature extraction, similarity/distance measures and user
interaction. The way the user communicates with the
content based image retrieval system, the size of the
databases, the features used and the speed of the retrieval
are the most important factors that judge the success of a
CBIR system. Moreover, it also reveals that the how
algorithms are used when SVM classifier is used for the
extraction of various features of images to obtain the
desired results as per the user’s requirement.
REFERENCES
International Journal of Computer Science and Mobile Computing,
International Journal of Computer Science and Mobile Computing,, pg.
769-775, 2014.
Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng
Fundamentals Of Content-Based Image Retrieval.
da Silva Torres, Ricardo and Falc,Content-Based Image Retrieval: Theory
and Applications, Institute of Computing, State University of Campinas,
Campinas, SP, Brazil,vol.13,no.2,pp.161-185,2006.
Fundamental of Content Based Image Retrieval, International Journal of
Computer Science and Information Technologies,no.3260 – 3263,2012.
M.E. El Alami,A new matching strategy for content based image retrieval
system , , Applied Soft Computing,vol.14,pp.407-418,2014
Paper Technique
Dataset
(Accuracy)
Sultan Aljahdali
(2012)
Gabor Filter
COIL Dataset
(89.5%)
Sukhmanjeet Kaur
(2015)
SURF(Speeded Up
Robust Feature)
98%
M.E ElAlami
ANN(Feed Forward
Neural Network)
Wang dataset
(67.2%)
GUI-ZHI LI,YA-HUI
LIU,CHANG-
SHENG-ZHOU(2013)
Semi Supervised
Approach
Corel Dataset
Rajesh Singla and
Haseena B.A(2014)
Fast Fourier transform 85.5%
Xiukuan Zhao (2011)
Bearing fault Diagnosis
and Gear Fault
-
K.Ashok Kumar &
Y.V.Bhaskar
Reddy(2012)
Quadratic Distance
Metric Algorithm
Corel Image
dataset &
benchmark dataset
S. Mangijao Singh &
K.
Hemachandran(2012)
Canberraa Distance And
Gabor Wavelet
Wang dataset
Sigmoidal
Function
h(.)
h(.)
X1
1
X2
xn
1

4. Int. Journal of Electrical & Electronics Engg. Vol. 2, Spl. Issue 1 (2015) e-ISSN: 1694-2310 | p-ISSN: 1694-2426
119 NITTTR, Chandigarh EDIT-2015
Jun Wu a,n, HongShen a ,b, Yi-DongLi a, Zhi-BoXiao c, Ming-YuLu c,
Chun-LiWang Learning a hybrid similarity measure for image
retrieval, Pattern Recognition,vol.46,no.11,pp.2927-2939,2013.
Content Based Image Retrieval – A Literature Review, National
Conference on Computing, Communication and Control,2012.
Ying Liua,∗, Dengsheng Zhanga , Guojun Lua, Wei-Ying Mab ,A
survey of content-based image retrieval with high leval
semantics,vol.40,no.1,pp.262-282,2007.
Miguel Arevalillo-Herráez, Francesca J. Ferri,An Improved distance-
based relevance feedback strategy for image
retrieval,vol.31,no.10,pp.704-713,2013.
Wang, Zhou and Bovik, Alan C and Sheikh, Hamid R and Simoncelli,
Eero P,Image Quality Assessment: From Error Visibility to Structural
Similarity.IEEE Transactions On Image Processing,vol.13,no.4,pp.600-
612,2014.
Hsiao, Mann-Jung and Huang, Yo-Ping and Tsai, Tienwei and Chiang,
Te-Wei ,An Efficient and Flexible Matching Strategy for Content-based
Image Retrieval , Life Science Journal,vol.7,no.1,pp.99-106,2010.
R. Venkata Ramana Chary, Dr. D. Rajya Lakshmi Image Retreival And
Similarity Measurement Based On Image Feature, IJCST 4, 2011.
Reddy, P Vijaya Bhaskar and Reddy, A Rama Mohan,Content based
image indexing and retrieval using directional local extrema and
magnitude patterns.vol.68,no.7,pp.637-643,2014.
Liu, Ying and Zhang, Dengsheng and Lu, Guojun and Ma, Wei-Ying, A
survey of content-based image retrieval with high-level
semantics,vol.40,no.1,pp.262-282,2007.
Singh, Nidhi and Singh, Kanchan and Sinha, Ashok K. A Novel
Approach for Content Based Image Retrieval,vol.4,pp.245-250,2012.
Wu, Jun and Shen, Hong and Li, Yi-Dong and Xiao, Zhi-Bo and Lu,
Ming-Yu and Wang, Chun-Li,,Learning a hybrid similarity measure for
image retrieval,vol.46,no.11,pp.2297-2939,2013.
Aljahdali, Sultan and Ansari, Aasif and Hundewale, Nisar},Classification
of Image Database using SVM with Gabor Magnitude,pp.126-
132,2012,IEEE.
Sukhmanjeet Kaur, Mr. Prince Verma,Content Based Image Retrieval:
Integration of Neural Networks Using Speed-Up Robust Feature and
SVM, (IJCSIT) International Journal of Computer Science and
Information Technologies.
Ashok Kumar & Y.V.Bhaskar Reddy, Content Based Image Retrieval
Using SVM Algorithm, International Journal of Electrical and Electronics
Engineering (IJEEE) ISSN
Sanchita Pange and Sunita Lokhande,Image Retrieval system by using
CWT and Support vector Machines,Signal & image processing:,An
international Joural(SIPIJ)
Kaur Bhavneet & Jindal Sonika, An implementation of feature extraction
over medical images on open Cv environment,(IEEE)