Content Based Image Retrieval : Classification Using Neural Networks

The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.5, October 2014
CONTENT BASED IMAGE RETRIEVAL:
CLASSIFICATION USING NEURAL NETWORKS
Shereena V.B.1and Julie M. David2
1,2Asst.Professor, Dept of Computer Applications, MES College, Marampally,
Aluva, Cochin, India
ABSTRACT
In a content-based image retrieval system (CBIR), the main issue is to extract the image features that
effectively represent the image contents in a database. Such an extraction requires a detailed evaluation of
retrieval performance of image features. This paper presents a review of fundamental aspects of content
based image retrieval including feature extraction of color and texture features. Commonly used color
features including color moments, color histogram and color correlogram and Gabor texture are
compared. The paper reviews the increase in efficiency of image retrieval when the color and texture
features are combined. The similarity measures based on which matches are made and images are
retrieved are also discussed. For effective indexing and fast searching of images based on visual features,
neural network based pattern learning can be used to achieve effective classification.
KEYWORDS
CBIR, Color moments, Color histogram, Color correlogram, Gabor filter, Precision, Recall, Classification,
Neural Network.
1. INTRODUCTION
Image Processing involves changing the nature of an image in order to improve its pictorial
information for human interpretation and render it more suitable for autonomous machine
perception [1].The advantage of image processing machines over humans is that they cover
almost the entire electromagnetic spectrum, ranging from gamma to radio waves where as human
eye is limited to the visual band of the electromagnetic spectrum. They can operate on images
generated by sources like ultrasound, electron microscopy, and computer-generated images. Thus
image processing has an enormous range of applications and almost every area of science and
technology such as medicine, space program, agriculture, industry and law enforcement make use
of these methods. One of the key issues with any kind of image processing is image retrieval
which is the need to extract useful information from the raw data such as recognizing the
presence of particular color or textures before any kind of reasoning about the image’s contents is
possible.
Early work on image retrieval can be traced back to the late 1970s. In 1979, a conference on
Database Techniques for Pictorial Applications was held in Florence [2]. Early techniques were
not generally based on visual features but on the textual annotation of images, where traditional
database techniques are used to manage images. Many difficulties were faced by text based
retrieval, since volume of digital images available to users increased dramatically. The efficient
management of the rapidly expanding visual information became an urgent problem. This need
DOI : 10.5121/ijma.2014.6503 31

formed the driving force behind the emergence of content-based image retrieval techniques
(CBIR).
CBIR is a technique which uses visual contents to search images from an image database. In
CBIR, visual features such as colour and texture are extracted to characterise images. CBIR
draws many of its methods from the field of image processing and computer vision, and is
regarded as a subset of that field. In CBIR, visual contents are extracted and described by
multidimensional feature vectors. To retrieve images, users provide the retrieval system with
example images. The system changes them into internal representation of feature vectors. The
similarities or differences between feature vectors of the query examples and those of the images
in the database are calculated and retrieval performed with an indexing scheme .The indexing
scheme is an efficient way to search for image database. Recent retrieval systems have
incorporated user’s relevance feedback to modify the retrieval process.
The tasks performed by CBIR can be classified into Pre-processing and Feature extraction stages.
In Pre-processing stage, removal of noise and enhancement of some object features which are
relevant to understanding the image is performed. Image segmentation is also performed to
separate objects from the image background. In Feature Extraction stage, features such as shape,
colour, texture etc. are used to describe the content of the image. This feature is generated to
accurately represent the image in the database. The colour aspect can be achieved by the
techniques like moments, histograms and correlograms. The texture aspect can be achieved by
using transforms or vector quantization. Similarity Measurement is also done in this stage. ie. the
distance between query image and different images in the database is calculated and the one with
the shorter distance is selected [3]. Similarity measurement can be formulated as follows.
32
Let { F(x,y):x=1,2,…X, y=1,2,….Y}be a 2D image pixel array.
For color images , F(x,y) denotes the color value at pixel (x,y)
ie,{F(x,y)=FR(x,y),FG(x,y), FB(x,y)}
For black and white images F(x,y) denotes the grayscale intensity at (x,y).
The problem of image retrieval can be quoted mathematically as follows:
For a query image Q, we find an image T from the image database such that the distance between
corresponding feature vectors is less than the specified threshold t.
ie,D(Feature(Q),Feature(T))<=t
There is a lot of research being done in the field of CBIR in order to generate better
methodologies for feature extraction. In this paper, a study of different color and texture
descriptors for content-based image retrieval is carried out to find out whether a combination of
different features gives better results.
One major limitation of CBIR is the failure to retrieve semantically similar images since only low
level image features are extracted. In order to make the retrieval results more satisfactory, high-level
concept-based indexing must be considered. In this paper, we present a study on efficient
image retrieval based on classification using neural networks [4].
Neural networks are a promising alternative to various conventional classification methods due to
the following advantages [5]. First, neural networks are data driven self-adaptive methods in that
they can adjust themselves to the data without any explicit specification of functional or
distributional form for the underlying model. Second, they are universal functional approximates

in that neural networks can approximate any function with arbitrary accuracy. Third, neural
networks are nonlinear models, which makes them flexible in modelling real world complex
relationships. Finally, neural networks are able to estimate the posterior probabilities, which
provide the basis for establishing classification rule and performing statistical analysis.
The rest of this paper is organized as follows. In Section 2, we discuss previous work in CBIR. In
Section 3, we explain Feature extraction and representation methods. Section 4 explains
Combination of features, Section 5 explains Classification of images, Section 6 explains
Performance evaluation and indexing schemes and finally, Conclusions are given in Section 7.
33
2. LITERATURE REVIEW
Researchers have proposed different methods to improve the system of content based image
retrieval. Ryszard S. Chora´s [3] stated in his paper that the similarity of the feature vectors of the
query and database images is measured to retrieve the image. M. Stricker, and M. Orengo, have
shown that [6] the first order (mean), the second (variance) and the third order (skewness) color
moments have been proved to be efficient and effective in representing color distributions of
images. In his paper J. Huang, et al., [7] proposed the color correlogram to characterize not only
the color distributions of pixels, but also the spatial correlation of pairs of colors. Deepak S.
Shete1, Dr. M.S. Chavan [8] proposed that the ability to match on texture similarity can often be
useful in distinguishing between areas of images with similar color (such as sky and sea, or leaves
and grass). Fazal Malik, Baharum Baharudin [9] proposed a CBIR method which is based on the
performance analysis of various distance metrics using the quantized histogram statistical texture
features. The similarity measurement is performed by using seven distance metrics. The
experimental results are analysed on the basis of seven distance metrics separately using different
quantized histogram bins such that the Euclidean distance has better efficiency in computation
and effective retrieval. This distance metric is most commonly used for similarity measurement
in image retrieval because of its efficiency and effectiveness.
In the paper of Manimala Singha and K. Hemachandran [10], they presented a novel approach for
Content Based Image Retrieval by combining the color and texture features called Wavelet-Based
Color Histogram Image Retrieval (WBCHIR). Similarity between the images is ascertained by
means of a distance function. The experimental result shows that the proposed method
outperforms the other retrieval methods in terms of Average Precision. Md. Iqbal Hasan Sarker
and Md. Shahed Iqbal [11] proposed that using only a single feature for image retrieval may be
inefficient. They used color moments and texture features and their experiment results
demonstrated that the proposed method has higher retrieval accuracy than the other methods
based on single feature extraction. N.R. Janani and Sebhakumar P. suggests [12] a content-based
image retrieval method which combines color and texture features in order to improve the
discriminating power of color indexing techniques and also a minimal amount of spatial
information is encoded in the color index.
Arvind Nagathan , Manimozhi and Jitendranath Mungara stated in their paper [13] that the use of
neural network has considerably improved the recall rate and also retrieval time, due to its highly
efficient and accurate classification capability. They used a three layer neural network as
classifier which is set up and configured with parameters that are best suitable for image retrieval
task. B. Darsana and G. Jagajothi [14] used the neural network classification method in their
paper for effective retrieval of images. In their paper they justify that the neural network
classification method achieves the goals of clustering relevant images using meta-heuristics and
dynamically modifies the feature space by feeding automatic relevance feedback without any
human interaction. The motivation behind this paper is a study on the works done by early

researchers in the field of content based image retrieval based on color and texture features and
the neural network classification for efficient image retrieval.
34
3. FEATURE EXTRACTION AND REPRESENTATION
Features are properties of images such as colour, texture, shape, edge information extracted with
image processing algorithms. A single feature does not give accurate results, but a combination of
features is minimally needed to get accurate retrieval results.
3.1 Color
The most widely used visual feature in image retrieval is color feature. Color feature is relatively
robust to background complications. Each pixel can be represented as a point in 3D color space.
Commonly used color space include RGB, CIELab where “L” value for each scale indicates the
level of light or dark, “a” value redness or greenness, and “b” value yellowness or blueness, HSV
(Hue, Saturation, Value).
In the RGB color space, a color is represented by a triplet (R,G,B), where R gives the intensity of
the red component, G gives the intensity of the green component and B gives the intensity of the
blue component. The CIE Lab spaces are device independent and considered to be perceptually
uniform. They consist of a luminance or lightness component (L) and two chromatic components
a and b or u and v. HSV (or HSL, or HSB) space is widely used in computer graphics and is a
more intuitive way of describing color. The three color components are hue, saturation (lightness)
and value (brightness). HSV colour model describes colours in terms of their shades and
brightness (Luminance). This model offers a more intuitive representation of relationship between
colours. Basically a colour model is the specification of coordinate system and a subspace within
that, where each colour is represented in single point. Hue represents the dominant wavelength in
light. It is the term for the pure spectrum colours. Hue is expressed from 0º to 360º. It represents
hues of red (starts at 0º), yellow (starts at 60º), green (starts at 120º), cyan (starts at 180º), blue
(starts at 240º) and magenta (starts at 300º).Eventually all hues can be mixed from three basic
hues known as primaries. Saturation represents the dominance of hue in colour. It can also be
thought as the intensity of the colour. It is defined as the degree of purity of colour. A highly
saturated colour is vivid, whereas a low saturated colour is muted. When there is no saturation in
the image, then the image is said to be a grey image. Value describes the brightness or intensity of
the colour. It can also be defined as a relative lightness or darkness of colour [15].The HSV
values of a pixel can be transformed from its RGB representation according to the following
formula:
= cos12
−
) +
− )]
√[
−
) +
− )

− )]
= 1 − 3[
,
, )]
+
+ = +
+
3
Once the colour space is specified, colour feature is extracted from images or regions. A number
of important colour features have been proposed in the literatures, including color moments
(CM), color histogram, color correlogram etc. The Color moment can be used as remedies of
user’s queries which are semantic in nature. Color histogram is a popular color feature that has
been widely used in many image retrieval systems. Color histogram is robust with respect to
viewpoint axis and size, occlusion, slow change in angle of vision and rotation. The color
correlogram was proposed to characterize not only the color distributions of pixels, but also the

The International Journal of Multimedia Its Applications (IJMA) Vol.6, No.5, October 2014
spatial correlation of pairs of colors. Compared to the color histogram the color correlogram
provides the best retrieval results, but is also the most computational expensive due to its high
dimensionality.
3.1.1.Color moments
To differentiate objects based on color, Color moments have been successfully used in many
retrieval systems, especially when the image contains just the object. The basis of color moments
is that the distribution of color in an image can be considered as a probability distribution which
can be characterized by various moments. ie. If the color in an image follows a certain probability
distribution, the image can be identified by that distribution using moments. The first order
(mean), the second order (variance) and the third order (skewness) color moments have been
proved to be efficient and effective in representing color distributions of images [6].
35
=1
$
#%
!#
=
1
$
#%
!

# − )) '(
=
1
$
#%
!

# − ))) '*
Where Pij is the value of the ith color channel of image pixel j and N is the number of pixels in the
image.
A color can be defined by 3 or more values. Here we can use any of the color coding schemes,
say HSV. A moment can be calculated for each of these channels. Thus we get nine numbers—
three moments for each color channel as color features for each of the image. Thus color
moments are a very compact representation compared to other color features. Due to this
compactness, it may also lower the discrimination power.
Similarity between two image distributions is defined as the sum of weighted differences between
the moments of two distributions.
ie.
0
%
+,-,
, .) =!/
12 − 21 + /1 − 1 + /)1 − 1
where (H,I) are the two image distribution components, i is the current channel index (1=H, 2=S,
3=V), r is the number of channels ,here 3,2,2are the first order moments of two image
distributions , , are the second order moments of two image distributions, , are the third
order moments of the two image distributions and / are the weights for each moment. Pairs of
images are ranked based on +,-, values. The images with lower +,-, values are ranked high
and are more similar compared to those with higher +,-, values.
The methodology used to calculate moments is as follows. We first scale all images to the same
size for efficiency. Color moments are based on probability distributions. So image size should
not change the result of comparison. We calculate the three color moments using the formula
defined above for the Query Image. We then repeat the calculations for our database images.
Calculate d454value after giving appropriate weights and rank the images in the increasing
order of this value. The images with the lowest d454 values are selected as the result images. In
this way, we can use color moments as a technique to compare images based on color. Color
moments can be used as the first pass to narrow down the search space before other sophisticated
color features are used for retrieval.

3.1.2. Color Histogram
Color Histogram represents the distribution of intensity of the color in the image. Color
histograms are a set of bins where each bin denotes the probability of pixels in the image being of
a particular color. It serves as an effective representation of the color content of an image if the
color pattern is unique compared with the rest of the data set. In addition, it is robust to translation
and rotation about the view axis and changes only slowly with the scale, occlusion and viewing
angle [3].
A color histogram H for a given image is defined as a vector
H = {H[1], H[2], . . . H[i], . . . , H[N]}
where i represent a color in the color histogram, H[i] is the number of pixels in color i in that
image, and N is the number of bins in the color histogram, i.e., the number of colors in the
adopted color model.
In order to compare images of different sizes, color histograms should be normalized. The
normalized color histogram H is defined as
36
H = 7H[0],H[1],…H[i], . .H[N]=
where H[i] = [?]
@A ,XY is the total number of pixels in an image.
An ideal color space quantization presumes that distinct colors should not be located in the same
sub-cube and similar colors should be assigned to the same sub-cube. A color histogram with few
colors will decrease the possibility that similar colors are assigned to different bins, but it
increases the possibility that distinct colors are assigned to the same bins, and that the information
content of the images will decrease by a greater degree. color histograms with a large number of
bins will contain more information about the content of images, thus decreasing the possibility of
distinct colors will be assigned to the same bins.
Minkowski-form distance metrics [16] compare only the same bins between color histograms and
are defined as:
E
%
+
B, .) =!|D
[] − F[]|0
Where Q and I are two images, N is the number of bins in the color histogram (for each image we
reduce the colors to N, in the RGB color space, so each color histogram has N bins), HQ[i] is the
value of bin i in color histogramHG , which represents the image Q, and HI[i] is the value of bin i
in color histogram HI which represents the image I.
When r=1, the Minkowski-form distance metric becomes L1 . When r=2, the Minkoski-form
distance metric becomes the Euclidean distance. This Euclidean distance can be treated as the
spatial distance in a multi-dimensional space. In this paper, we will use the square root of
Euclidean distance to calculate the distance between two color histograms, which is defined as:
E
%
+
B, .) = √!|D
[] − F[]|
The image retrieval using histogram consists of the following stages. First of all Query image is
given from the user. Then the histogram of the color image is calculated. Each image added to the
database is analysed and a colour histogram is computed which shows the proportion of pixels of
each colour within the image. Then the colour histogram for each image is stored in the database.
Finally Euclidean Distance from query image to database images is calculated and sorted the
distance in ascending order and the top images are displayed on the screen. Thus we can use color

histograms to retrieve matching images from the database. It performs well compared to other
descriptors when images have mostly uniform color distribution but it has the disadvantages of
lack of spatial information and therefore tends to give poor results. If two images have exactly
the same color proportion but the colors are scattered differently, then we can’t retrieve correct
images using color histogram. In such cases several improvements have been proposed to
incorporate spatial information. A simple approach is to divide an image into subareas and
calculate histogram for those sub areas.
3.1.3.Color Correlogram
A color correlogram is a table indexed by color pairs, where the kth entry for (i, j) specifies the
probability of finding a pixel of color j at a distance k from a pixel of color i in the image [7]. Let
I represent the entire set of image pixels and Ic(i) represent the set of pixels whose colors are
c(i).Then, the color correlogram is defined as:
37
H
,#)
I) = JK∈M
F),K∈F [N2 ∈ .M
#)CN1 − N2C = I]
Where i, j ∈ {1, 2, …, N}, k∈ {1, 2, …, d}, and | p1 – p2 | is the distance between pixels p1 and
p2.
If we consider all the possible combinations of color pairs the size of the color correlogram will
be very large (O(N2d)), therefore a simplified version of the feature called the color
autocorrelogram is often used instead. The color autocorrelogram only captures the spatial
correlation between identical colors and thus reduces the dimension to O(Nd) [7].
L1 and L2 distance metrics in Minkowski-form distance metrics [16] are used to compare color
features of two images. For correlograms, L1 is used in most cases because it is simple and
robust. The distance between two images I and I’ is calculated as follows:
C. − .|O,P =! |ℎMR
.) − ℎMRS.T| ∈[,]
|. − .|U,P =! |HMR,MV
W),
.) − HMR,MV
W),
.)| ,#∈[,],X∈[Y]
The image retrieval problem in color correlogram is as follows. A Query image is given from the
user. Then the correlogram of the color image is calculated. Color correlograms of the database
images are also calculated. Then the distance from query image to database images is calculated
using L1 metric and sorted the distance in ascending order and the top images are displayed on
the screen. Thus we can use color correlograms to retrieve matching images from the database.
3.2 Texture
Texture is another property of image which is used in pattern recognition and computer vision.
Texture [17] is defined as structure of surfaces formed by repeating a particular element or
several elements in different relative spatial positions. The repetition involves local variations of
scale, orientation, or other geometric and optical features of the elements. The ability to match on
texture similarity can often be useful in distinguishing between areas of images with similar color
(such as sky and sea, or leaves and grass) [8].Thus texture analysis plays an important role in
comparison of images supplementing the color feature. Texture representation methods can be
classified into Structural and Statistical categories. Structural methods are applied to textures that
are very regular. Statistical methods, includes characterizing texture by the statistical distribution
of the image intensity.
Many Statistical techniques has been used for measuring texture similarity in which the best
established rely on comparing values of second order statistics calculated from query and stored

images [15]. These techniques calculate the relative brightness of selected pairs of pixels from
each image. From these it is possible to calculate measures of image texture such as the degree of
contrast, coarseness, directionality and regularity, or periodicity, directionality and randomness.
Alternative methods of texture analysis for retrieval include the use of Gabor filters and
Wavelets. Texture queries can be formulated in a similar manner to colour queries, by selecting
examples of desired textures from a palette, or by supplying an example query image.
3.2.1. Gabor filter
The Gabor filter is a statistical method that has been widely used to extract texture features [18].
This is the most frequently used method in image retrieval by texture. Gabor Filters are a group of
wavelets where each wavelet capturing energy at a specific frequency and orientation. There have
been many approaches proposed to characterize textures of images based on Gabor filters. In
most of the CBIR systems based in Gabor wavelet, the mean and standard deviation of the
distribution of the wavelet transform coefficients are used to construct the feature vector [19].
The basic idea of using Gabor filters to extract texture features is as follows.
A two dimensional Gabor function g(x, y) is defined as:
Q=[ mm,mm,m, m …. . , )z, )z ] denote texture feature vector of query image
38
Z
[, =
1
2]^_
`[N a−b
1
2
[
^
cde

_
f + e
f + 2]g/^hi
Where ^ j+ _ are the standard deviations of the Gaussian envelopes along the x and y
direction.
Given and image I(x,y) it’s Gabor transform is defined as
∗
[ − [, − +[+
/,$
[, = k.
[, Z,$
Where * indicates the complex conjugate. Then the mean ,$and the standard deviation ,$of
the magnitude of /,$
[,
i.e. f=[ mm,mm,….,$, ,$, nX, nX ] can be used to represent the feature of a
homogeneous texture region.
The texture similarity measurement of a query image Q and a target image T in the database is
defined by
+
B, o = p,p$ +,$
B, o
Where +,$ =
u rst
u qx1rsv t1 + q
yst
q
rst
v w|
qrst
u ysv tw|
qyst
u qx1ysv t1
If fg
andfg
T=[mm,mm,m, m …. . , )z, )z ] denote texture feature vector of database image, then
distance between them is given by:
D − {|}|
|{|
+ =! |{|
D| − |{|}|
Y
%
The Canberra distance measure is used for similarity expression. In the case of low level texture
feature, we apply Gabor filters on the query image and we obtain an array of magnitudes. The
mean μ4~ and standard deviation 4~ of the magnitudes are used to create a texture feature
vector fg. Similarly the Gabor filters of database images are also calculated and Canberra distance

measure is used for computing the distance between query and database images and the results of
a query are displayed in decreasing similarity order. In this way Gabor filter can be used to match
images from the database using texture property of the image.
3.2.2 Haar wavelet Transforms
Wavelet transforms provide a multi-resolution approach to texture analysis and classification. The
wavelet transform represents a function as a superposition of a family of basic functions called
wavelets. The wavelet transform computation of a two-dimensional image is also a multi-resolution
approach, which applies recursive filtering and sub-sampling. At each level, the image
is decomposed into four frequency sub-bands, LL, LH, HL, and HH where L denotes low
frequency and H denotes high frequency.
If a data set Xo, X1,…Xn-1 …. Contains N elements [10], there will be N/2 averages and N/2
wavelet coefficient values. The averages are stored in the first half of the N element array, and the
coefficients are stored in the second half of the N element array. The averages become the input
for the next step in the wavelet calculation. The Haar equations to calculate an average and a
wavelet coefficient from an odd and even element in the data set are
39
j =

+ x
2
€ =

− x
2
For a 1D Haar transform of an array of N elements, find the average of each pair of elements, find
the difference between each pair of elements and divide it by 2, fill the first half of the array with
averages, fill the second half of the array with coefficients and Repeat the process on an average
part of the array until a single average and a single coefficient are calculated. For a 2D Haar
transform, Compute 1D Haar wavelet decomposition of each row of the original pixel values and
then compute1D Haar wavelet decomposition of each column of the row-transformed pixels. Red,
green and blue values are extracted from the images. Then we apply the 2D Haar transform to
each color matrix.
We apply Haar wavelet decomposition of an image in the RGB color space. We continue
decomposition up to level 4, and with F-norm theory we decrease the dimensions of image
features and perform highly efficient image matching. If A is a square matrix and Ai is its ith
order sub-matrix where
j,…j$
j$,…j$$
= ‚
j,…j
j,…j
ƒ = ‚
ƒ = 1~
F-norm of Ai is
||||… − ||||… and ||||…= 0, we can define feature vector of A as
{†… = {Δ, Δ,…. Δ$}
The similarity between two images is computed by calculating the distance between feature
representation of the query image and feature representation of the image in the dataset. We use
Canberra distance for distance calculation of the feature vectors.
+
Š, +) = p |‹RYR|
|‹R||YR|
$
% , where
q= (q1,q2,…qn) is the feature vector of the query image,
d= (d1 ,d2,…dn) is the feature vector of the image in the database,
n = number of elements of the feature vector.

A feature vector is extracted from each image in the database and the set of all feature vectors is
organized as a database index. When similar images are searched with a query image, a feature
vector is extracted from the query image and is matched against the feature vectors in the index.
If the distance between feature representation of the query image and feature representation of the
database image is small, then it is considered similar. Thus we can use Haar wavelet for matching
images from the database.
4. COMBINING THE FEATURES
The image retrieval using only single feature such as color moment or color histogram may be
inefficient. It may either retrieve images not similar to query image or may fail to retrieve images
similar to query image. To produce efficient results, we use combination of color and texture
features. The similarity between query and target image is measured from two types of
characteristic features which includes color and texture features. Two types of characteristics of
images represent different aspects of property. While calculating similarity measure, appropriate
weights are considered to combine the features [11]. The distance between the query image and
the image in the database is calculated as follows:
40
d =w1*d1+w2*d2.
Here, w1 is the weight of the color features, w2 is the weight of the texture features and d1 and d2
are the distances calculated using color features and texture features respectively. The distance d
is calculated for each query image with all images in the database. The image that has a lower
distance value is considered the similar image and the results are ranked in the ascending order of
d. From the studies, [20] It is seen that the value of the average precisions based on single
features i.e. only Gabor texture features or only Color moments are less than the average
precisions of combined features of color moments and Gabor texture features. This shows that
there is considerable increase in retrieval efficiency when both color and texture features are
combined for CBIR. Also it is found that [8] the texture and color features are extracted through
wavelet transformation and color histogram and the combination of these features is a faster
retrieval method which is robust to scaling and translation of objects in an image.
5. CLASSIFICATION OF IMAGES
The nearest images obtained using feature extraction techniques are routed to Neural Network
classification [13]. Neural Networks are very effective in case of classification problems where
detection and recognition of target is required. It is preferred over other techniques due to its
dynamic nature of adjusting the weights according to final output and applied input data. This
adjustment of weights takes place iteratively until desired output is obtained. And this weight
adjustment of network is known as learning of neural network.
The architecture of neural network consists of a large number of nodes and interconnection of
nodes. A multiple-input neuron with multiple inputs ‘R’ is shown in Figure 1. The individual
inputs P1, P2…., PR are weighted by corresponding elements W, ,W,,…,W, of the weight
matrix W.
The neuron also has a bias ‘b’, which is summed with the weighted inputs to form the net input
‘n’:
n = W, . P + W,P + ⋯ W,P + b.
In matrix form, this can be rewritten as
n = W. P + b

41
Now, the neuron output is given as,
a = f
W. P + b)
The transfer function used above is a log-sigmoid transfer function. This transfer function takes
the input (which may have any value between plus and minus infinity) and squashes the output in
between 0 to 1 range, according to the expression
y = 1

1 + e~)
The nodes at a particular stage constitute a layer. The first layer is called input layer and last layer
is called output layer. The layers in between output and input layer are called hidden layers. As
the number of hidden layers in the network increases, the performance of network increases. Each
node in a network serves the purpose of summation of all its inputs. The output of a node is
further applied to the next node.
The retrieved images are classified using three layer neural network. The first layer has input
neurons which send data via synapses to the second layer of neurons, and then via more synapses
to the third layer of output neurons [14] .The synapses store parameters are actually weights that
manipulate the data in the calculations. In each iteration, the weights of interconnections are
updated for efficient retrieval. The next process is the clustering of the accumulated images into
positive and negative feedback. The images obtained are routed to fuzzy c-means clustering
algorithm. The positive and negative relevance of every image with the query image is analysed.
Accordingly, relevant and irrelevant image subsets are created, which will be progressively
populated across iterations, based on the change in weights of individual features, thus changing
the distance between the query image and the database images. This will help in retrieving the
exact query image from the database. The Relevance Feedback based similarity technique is used
where in each iteration the feature weights are updated. The number of output images required
can be controlled by the user.
6. PERFORMANCE EVALUATION AND INDEXING SCHEMES
The performance of retrieval of the system can be measured in terms of its recall and precision.
Recall measures the ability of the system to retrieve all the models that are relevant, while
precision measures the ability of the system to retrieve only the models that are relevant [8].
J`€–— = ˜™`J —{ J`š`›jœ jZ`– J`œJ`›`+
o—œjš ˜™`J —{ jZ`– J`œJ`›`+
`€jšš = ˜™`J —{ J`š`›jœ jZ`– J`œJ`›`+
o—œjš — —{ J`š`›jœ jZ`–
The number of relevant items retrieved is the number of the returned images that are similar to
the query image in this case. The total number of items retrieved is the number of images that are
returned by the search engine. In precision and recall, crossover is the point on the graph where
the both precision and recall curves meet. The higher the number of crossover points better will
be the performance of the system.
The average precision for the images that belongs to the qth category (Aq) has been computed by
= p
ž
X∈†Ÿ |†Ÿ|Where q=1, 2……10.
Finally, the average precision is given by:

42
=!

‹/1
m
‹%
0
Another important issue in content-based image retrieval is effective indexing and fast searching
of images based on visual features. The feature vectors of images tend to have high
dimensionality and are not well suited to traditional indexing structures. So dimension reduction
is usually used before setting up an efficient indexing scheme. One of the techniques commonly
used for dimension reduction is principal component analysis (PCA). It is a general and very
recognizable method [21] and an optimal technique that linearly maps the input data to a
coordinate space such that the axes are aligned to reflect the maximum variations in the data.
Using PCA, the original data can be projected onto a much smaller space, resulting in
dimensionality reduction [22]. The QBIC system uses PCA to reduce a 20-dimensional shape
feature vector to two or three dimensions [23].
After dimension reduction, the multi-dimensional data are indexed. A number of approaches have
been proposed for this purpose, including R-tree [24], linear quad-trees [25]. Most of these multi-dimensional
indexing methods have reasonable performance for a small number of dimensions
(up to 20), but explore exponentially with the increasing of the dimensionality and eventually
reduce to sequential searching. Furthermore, these indexing schemes assume that the underlying
feature comparison is based on the Euclidean distance, which is not necessarily true for many
image retrieval applications. One attempt to solve the indexing problems is to use hierarchical
indexing scheme based on the Self-Organization Map (SOM) proposed in [26].
7. CONCLUSION
This paper investigated various feature extraction algorithms in CBIR. A study of different color
and texture features for image retrieval in CBIR is performed. Numerous methods are available
for feature extraction in CBIR. They are identified and studied to understand the image retrieval
process in the CBIR systems. Studies made on experiment results show that the method based on
hybrid combination of color and texture features has higher retrieval accuracy than the other
methods based on single feature extraction. Color moments, color histograms, color correlogram
and gabor texture are considered for retrieval. It is difficult to claim that one feature is superior to
others. The performance depends on the color distribution of images. The combination of color
descriptors produces better retrieval rate compared to individual color descriptors. Color moments
and color histogram features can be combined to get better results. Color histograms and
correlograms can be combined retaining advantages of histograms with spatial layout. Similarly,
Texture feature can be combined with color moments or color histogram to get accurate results
for image retrieval. From the studies, it is found that only one color feature or texture feature is
not sufficient to describe an image. There is considerable increase in retrieval efficiency when
both color and texture features are combined.
Also we have reviewed various papers related to different classification methods for the
improvement of image retrieval in CBIR. Among different classification methods, Neural
Network classification is an efficient method for image retrieval. It takes into account the
characteristics of relevant and irrelevant images. Neural Network classification has considerably
improved the recall rate and also retrieval time, due to its highly efficient and accurate
classification capability.

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AUTHORS
Shereena V.B. received her MCA degree from Bharathidasan University, Trichy, India
in 2000. During 2000-2004, she was with Mahatma Gandhi University, Kottayam,
India as Lecturer in the Department of Computer Applications. Currently she is
working as Asst. Professor in the Department of Computer Applications with MES
College, Aluva, Cochin, India. Her research interests include Data Mining and Image
Processing.
Dr. Julie M. David completed her Masters Degree in Computer Applications and
Masters of Philosophy in Computer Science in the years 2000 and 2009 in Bharathiyar
University, Coimbatore, India and in Vinayaka Missions University, Salem, India
respectively. She has also completed her Doctorate in the research area of Artificial
Intelligence from Cochin University of Science and Technology, Cochin, India in 2013.
During 2000-2007, she was with Mahatma Gandhi University, Kottayam, India, as
Lecturer in the Department of Computer Applications. Currently she is working as an
Assistant Professor in the Department of Computer Applications with MES College, Aluva, Cochin, India.
She has published several papers in International Journals and International and National Conference
Proceedings. Her research interests include Artificial Intelligence, Data Mining, and Machine Learning.
She is a life member of International Association of Engineers, IAENG Societies of Artificial Intelligence
Data Mining, Computer Society of India, etc. and a Reviewer of Elsevier International Journal of
Knowledge Based Systems. Also, she is an Editorial Board Member of various other International Journals.
She has coordinated various International and National Conferences.

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