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CONTENT BASED IMAGE RETRIVAL THROUGH SEARCHING METHOD
CHAPTER 1
INTRODUCTION
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INTRODUCTION
1.1 Introduction to CBIR
Due to exponential increase of the size of the so-called multimedia files in recent years because of
the substantial increase of affordable memory storage on one hand and the wide spread of the World
Wide Web (www) on the other hand, the need for efficient tool to retrieve images from large dataset
becomes crucial. This motivates the extensive research into image retrieval systems. From historical
perspective, one shall notice that the earlier image retrieval systems are rather text-based search
since the images are required to be annotated and indexed accordingly. However, with the
substantial increase of the size of images as well as the size of image database, the task of userbased annotation becomes very cumbersome, and, at some extent, subjective and, thereby,
incomplete as the text often fails to convey the rich structure of the images. This motivates the
research into what is referred to as content-based image retrieval (CBIR) .
Content based image retrieval is based on (automated) matching of the features of the query image
with that of image database through some image-image similarity evaluation. Therefore, the images
will be indexed according to their own visual content in the light of the underlying (chosen) features
like color (distribution of color intensity across image, texture (presence of visual patterns that have
properties of homogeneity and do not result from the presence of single color, or intensity), shape
(boundaries, or the interiors of objects depicted in the image), or any other visual feature or a
combination of a set of elementary visual features. Needless to say, the advantages and end users of
such systems range from simple users searching a particular image on the web as well various type
of professional bodies, like police force for picture recognition, journalists requesting pictures that
match some query etc.
From historical perspective, probably the first use of CBIR goes back to Kato in early nineties
where he implemented what sounds to be the first automated image retrieval system using color and
shape features. Since Kato's pioneer work, many prototypes of CBIR systems have been developed,
and some of them did go to commercial market, e.g., IBM's QBIC system,
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which supports color, shape and texture feature, Virage developed by Virage Inc and supportscolor,
texture, color layout and shapes. However, it has been acknowledged the lack of maturity of current
technology, which limited its large scale deployment. This motivated the intensive research carried
out in many aspects of CBIR including image indexing, feature selection and extraction.
1.2 Problem Motivation
Image databases and collections can be enormous in size, containing hundreds, thousands or even
millions of images. The conventional method of image retrieval is searching for a keyword that
would match the descriptive keyword assigned to the image by a human categorizer . Currently
under development, even though several systems exist, is the retrieval of images based on their
content, called Content Based Image Retrieval, CBIR. While computationally expensive, the results
are far more accurate than conventional image indexing. Hence, there exists a tradeoff between
accuracy and computational cost. This tradeoff decreases as more efficient algorithms are utilized
and increased computational power becomes inexpensive.
1.3 Problem Statement
The problem involves entering an image as a query into a software application that is designed to
employ CBIR techniques in extracting visual properties, and matching them. This is done to retrieve
images in the database that are visually similar to the query image.
1.4. Proposed Solution
The solution initially proposed was to extract the primitive features of a query image and compare
them to those of database images. The image features under consideration were colour, texture and
shape. Thus, using matching and comparison algorithms, the colour, texture and shape features of
one image are compared and matched to the corresponding features of another image. This
comparison is performed using colour, texture and shape distance metrics. In the end, these metrics
are performed one after another, so as to retrieve database images that are similar to the query.
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CHAPTER 2
DETAILS OF THE PROJECT
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DETAILS OF THE PROJECT
2.1 Overview Of CBIR:
INPUT
DATA BASE
Figure 2.1:Block Diagram of image retrival system
Any CBIR system involves at least four main steps :

Feature extraction and indexing of image database according to the chosen visual
features,which form the perceptual feature space ,e.g. color, shape, texture or any
combination of the above.

Matching the query image to the most similar images in the data base according to
Some images.

Image similarity measure : This forms the search part of CBIR.

User interface and feedback which governs the display of the outcomes, their ranking
the type of the user interaction with possibility of refining the search through some
automatic or manual preference scheme.
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2.2. Feature extraction and indexing
One distinguishes two types of visual features in CBIR: primitive feature and domain-specific
features. The former includes color, shape and texture features while the latter includes, for
instance, face recognition, finger prints, handwriting, which form a sort of high level image
description or meta-object.
2.3 Color
Color represents one of the most widely used visual features in CBIR systems. First a color
space is used to represent color images. Typically, RGB (red Green Blue) space, where the grey
level intensity is represented as the sum of red, green and blue grey level intensities, is widely
used in practice. Next, a histogram, -in RGB space, one histogram for each basic color is
needed-, is employed to represent the distributions of colors in image. The number of bins of
the histogram determines the color quantization. Therefore, the histogram shows the number of
pixels whose grey level fails within the range indicated by the corresponding bin. The
comparison between images (query image and image in database) is accomplished through the
use of some metric which determines the distance or similarity between the two histograms.
However, it is straightforward to see that the restriction to distribution of colors only across the
whole image without accounting for spatial constraints is insufficient to discriminate between
images, as illustrated in Figure 2 whose two (distinct) images have the same color histogram.
Figure 2.2:Two
images with same
color histograms
One of the most important features that make possible the recognition of images by humans is
colour. Colour is a property that depends on the reflection of light to the eye and the processing of
that information in the brain. We use colour everyday to tell the difference between objects,
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and the time of day . Usually colours are defined in three dimensional colour spaces. These could
either be RGB (Red, Green, and Blue), HSV (Hue, Saturation, and Value) or HSB (Hue,saturation
and Brightness). The last two are dependent on the human perception of hue, saturation, and
brightness. Most image formats such as JPEG, BMP, GIF, use the RGB colour space to store
information . The RGB colour space is defined as a unit cube with red, green, and blue axes. Thus, a
vector with three co-ordinates represents the colour in this space. When all three coordinates are set
to zero the colour perceived is black.
2.3.1 Methods of Representation
The main method of representing colour information of images in CBIR systems is through colour
histograms. A colour histogram is a type of bar graph, where each bar represents a particular colour
of the colour space being used. In MatLab for example you can get a colour histogram of an image
in the RGB or HSV colour space. The bars in a colour histogram are referred to as bins and they
represent the x-axis. The number of bins depends on the number of colours there are in an image.
The y-axis denotes the number of pixels there are in each bin. In other words how many pixels in an
image are of a particular colour.
An example of a colour histogram in the HSV colour space can be seen with the following image:
Figure 2.3:Sample Image and its histogram
As one can see from the colour map each row represents the colour of a bin. The row is composed of
the three coordinates of the colour space. The first coordinate represents hue, the second saturation,
and the third, value, thereby giving HSV. The percentages of each of these coordinates are what
make up the colour of a bin. Also one can see the corresponding pixel numbers for each bin, which
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are denoted by the blue lines in the histogram. Quantization in terms of colour histograms refers to
the process of reducing the number of bins by taking colours that are very similar to each other and
putting them in the same bin. By default the maximum number of bins one can obtain using the
histogram function in MatLab is 256. For the purpose of saving time when trying to compare colour
histograms, one can quantize the number of bins. Obviously quantization reduces the information
regarding the content of images but as was mentioned this is the tradeoff when one wants to reduce
processing time. There are two types of colour histograms, Global colour histograms (GCHs) and
Local colour histograms (LCHs). A GCH represents one whole image with a single colour
histogram. An LCH divides an image into fixed blocks and takes the colour histogram of each of
those blocks . LCHs contain more information about an image but are computationally expensive
when comparing images. “The GCH is the traditional method for colour based image retrieval.
However, it does not include information concerning the colour distribution of the regions of an
image. Thus when comparing GCHs one might not always get a proper result in terms of similarity
of images.
EXAMPLES OF COLOUR HISTOGRAM
Figure 2.4:RGB and HSV Colour space
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2.4 Color layout
In this course, the spatial relationships are used in conjunction to color feature.
A natural way of doing so is by dividing the whole image into a set of sub images and takes the
color histogram of each sub image. Several variants of the proposal have been put forward
depending on the way the decomposition is accomplished. One shall mention, for instance, the
color auto-correlogram ,which expresses how the spatial correlation of pairs of colors change
with distance; that is, for a given distance d and row (i,j), it provides the probability of finding a
pixel of color j at distance d away from color i, where i and j stands for any pair of quantum
grey level intensity (or any bin in the histogram). Its computation complexity is O(k * n 2 ),
where k is the number of neighbour pixels, which is dependent on the distance selection.
Obviously such computation complexity grows fast when the distance becomes large. But it is
also linear to the size of the image.
Although the global color feature is simple to calculate and can provide reasonable
discriminating power in image retrieval, it tends to give too many false positives when the
image collection is large. Many research results suggested that using color layout (both color
feature and spatial relations) is a better solution to image retrieval. To extend the global color
feature to a local one, a natural approach is to divide the whole image into sub blocks and
extract color features from each of the sub blocks [1, 8]. A variation of this approach is the quad
tree-based color layout approach , where the entire image was split into a quad tree structure
and each tree branch had its own histogram to describe its color content. Although conceptually
simple, this regular sub block-based approach cannot provide accurate local color information
and is computation-and storage-expensive. A more sophisticated approach is to segment the
image into regions with salient color features by color set back-projection and then to store the
position and color set feature of each region. The advantage of this approach is its accuracy
while the disadvantage is the general difficult problem of reliable image segmentation. To
achieve a good trade-off between the above two approaches, several other color layout
representations were proposed.
2.5. Shapes
Shape may be defined as the characteristic surface configuration of an object; an outline or
contour. It permits an object to be distinguished from its surroundings by its outline . Shape
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representations can be generally divided into two categories 2: Boundary-based, and Regionbased.
Figure 2.5:Region based and boundary based shapes
Boundary-based shape representation only uses the outer boundary of the shape. This is done by
describing the considered region using its external characteristics; i.e., the pixels along the object
boundary. Region based shape representation uses the entire shape region by describing the
considered region using its internal characteristics; i.e., the pixels contained in that region.
A number of features characteristic of object shape (but independent of size or orientation) are
computed for every object identified within each stored image. Queries are then answered by
computing the same set of features for the query image, and retrieving those stored images whose
features most closely match those of the query.
2.6 Texture
Texture is that innate property of all surfaces that describes visual patterns, each having
properties of homogeneity. It contains important information about the structural arrangement of the
surface, such as; clouds, leaves, bricks, fabric, etc. It also describes the relationship of the surface to
the surrounding environment [2]. In short, it is a feature that describes the distinctive physical
composition of a surface. Texture properties include:
 Coarseness
 Contrast
 Directionality
 Line-likeness
 Regularity
 Roughness
Texture is one of the most important defining features of an image. It is characterized by the spatial
distribution of gray levels in a neighbourhood . In order to capture the spatial dependence of graylevel values, which contribute to the perception of texture, a two-dimensional dependence texture
analysis matrix is taken into consideration. This two-dimensional matrix is obtained by decoding the
image file; jpeg, bmp, etc.
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2.7 Methods of Representation
There are three principal approaches used to describe texture; statistical, structural and
spectral… Statistical techniques characterize textures using the statistical properties of the grey
levels of the points/pixels comprising a surface image. Typically, these properties are computed
using: the grey level co-occurrence matrix of the surface, or the wavelet transformation of the
surface.
Structural techniques characterize textures as being composed of simple primitive
structures called “texels” (or texture elements). These are arranged regularly on a surface according
to some surface arrangement rules. Spectral techniques are based on properties of the Fourier
spectrum and describe global periodicity of the grey levels of a surface by identifying high-energy
peaks in the Fourier spectrum .
Figure 2.6:Sample of Textures
For optimum classification purposes, what concern us are the statistical techniques of
characterization… This is because it is these techniques that result in computing texture properties…
The most popular statistical representations of texture are:

Tamura Texture

Discrete Wavelet Transform
2.7.1Tamura Texture
By observing psychological studies in the human visual perception, Tamura explored the texture
representation using computational approximations to the three main texture features of: coarseness,
contrast, and directionality [2, 12]. Each of these texture features are approximately computed using
algorithms.
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2.7.2 Discrete Wavelet Transform
Textures can be modeled as quasi-periodic patterns with spatial/frequency representation.
The wavelet transform transforms the image into a multi-scale representation with both spatial and
frequency characteristics. This allows for effective multi-scale image analysis with lower
computational cost . According to this transformation, a function, which can represent an image, a
curve, signal etc., can be described in terms of a coarse level description in addition to others with
details that range from broad to narrow scales
Unlike the usage of sine functions to represent
signals in Fourier transforms, in wavelet transform, we use functions known as wavelets. Wavelets
are finite in time, yet the average value of a wavelet is zero . In a sense, a wavelet is a waveform that
is bounded in both frequency and duration. While the Fourier transform converts a signal into a
continuous series of sine waves, each of which is of constant frequency and amplitude and of
infinite duration, most real-world signals (such as music or images) have a finite duration and abrupt
changes in frequency. This accounts for the efficiency of wavelet transforms. This is because
wavelet transforms convert a signal into a series of wavelets, which can be stored more efficiently
due to finite time, and can be constructed with rough edges, thereby better approximating real-world
signals. Generally we use haar wavelet
2.8 Semantic feature
So far the features described so far are based on primitive image features, referred to as level 1
feature. However, there are attempts to build bridge to level 2 type-features and image retrieval task.
An example of such system is the IRIS system , which uses color, texture, region and spatial
information to derive the most likely interpretation of the scene, generating text descriptors which
can then be input to any text retrieval system.
In contrast to these fully-automatic methods is a family of techniques which allow systems to
learn associations between semantic concepts and primitive features from user feedback. Examples
of such work include, where the user is invited to annotate selected regions of an image, and then
proceeds to apply similar semantic labels to areas with similar characteristics, which, in turn,
improves performance with additional user's feedback. Chang introduced the concept of semantic
visual template where the user is asked to identify a possible range of color, texture, shape or
motion parameters to express his or her query, which is then refined using
relevance feedback techniques and the query is given to semantic label and stored.
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CHAPTER 3
IMPLEMENTATION OF THE PROJECT
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IMPLEMENTATION OF THE PROJECT
3.1.Matching query to image
First, one notices that the query-image matching model is very much linked to the type of features
used to represent the images. One distinguishes at least three case Feature vector representation
where each component of the vector represents the value of a specific item or attribute of the image
feature, say Vi= [x1 x2 ...Xd] where d stands for number of attributes. For instance in case of greylevel color histogram, a component k represents the number of pixels whose values fall within the
range specified by the kth bin.
Region based representation where a set of vectors, possibly with different size, is used instead of a
single vector. A typical example includes color segmentation, where, for instance, the image is split
into four parts and the color histogram of each part is reported. Another example consists of use of
multiple features, so each vector would correspond to an individual feature.
Text-based representation where the summary of local feature vectors is rather described as textbased information or a sort of coding to report on the outcome of the feature extraction. This
corresponds to a high level description of the outcome.
Consequently, in each of the above case, the approach to the query-image matching is obviously
different.
3.2.Feature Extraction
Calculation of color histogram
Color is an important visual attribute for both human perception and computer vision and it is
widely used in image retrieval. The color histogram is one of the most direct and the most effective
color feature representation. This paper incorporates spatial information to it by combining the color
histograms for several sub-blocks defined in the minority clothing image. An appropriate color
space and quantization must be specified along with the histogram representation. In this paper,
three color spaces (RGB, HSV and CIE L*a*b*) with different quantification number are used to
test the performance of our Method. The experimental results in Tables 1-3 demonstrate that the
RGB color space with 8×4×4=128 quantification number is the best choice in our framework. For an
image with a size of M×N, we set the color quantification number to L and denote the image by the
equation
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.................................................(1)
We divide the image into n blocks. The color values of each block is denoted by
............................(2)Then the color histogram of each block is defined as
..................................(3)
where j num is the number of pixels in a sub-block whose color value is quantified to j.
3.3 Calculation of Edge Orientation Histogram
In the system of theory on computer vision, edge detection of image plays an important role.
This paper construct a feature descriptor namely edge orientation histogram, which can be seen as a
texture feature and also a shape feature. The classic edge detection operator are Sobel, Roberts,
Prewitt and Canny. Sobel is one of the most popular operator [22], which is named after Irwin Sobel
and Gary Feldman. The Sobel operator is based on convolving the image with a small, separable,
and integer valued filter in the horizontal and vertical directions and is therefore relatively
inexpensive in terms of computations. The operator uses two 3×3 kernels which are convolved with
the original image to calculate approximations of the derivatives - one for horizontal changes, and
one for vertical. If we define R, G, B as the unit vectors along the R, G, B axes in RGB color space,
the computations are as follows:
...........................................(4)
g(xx)and g( xy), g(yy)are defined as dot products of the vectors mentioned above:
..................................(5)
Using the above notations, it can be seen that the maximum gradient orientation of point (,)
(x ,y )is
...................................................(6)
And the gradient magnitude at (x, y) in the direction of (,) x y ϕ given by
.....................................(7)
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.............................(8)
3.4 Comprehensive feature representation
The comprehensive feature extraction algorithm proposed in this paper can be represented as
follows:
Step1: Divide the minority costume image with a size of M×N into n sub-blocks, the experimental
results in Table 4 demonstrate that our method gets the best results when n=2×2.
Step2: Calculate the color histogram of every sub-block and then linearly combine them as
................................(9)
Step3: Calculate the edge orientation histogram of each sub-block and then linearly combine them
as follows:
............................(10)
Step4: Linearly Combine all the histograms mentioned in step2 and step3 as
.............................................(11)
3.5 Similarity Measurement
After feature extraction, each image in the minority costume image dataset is represented as a
multidimensional feature vector .
.................................................(12)
If we use CSA and CSB to represent the dimension of color histogram HCxy and the dimension of
edge orientation histogram H x y ϕ respectively
M= CSA+ CSB dimensional feature vector will be extracted and stored for every image in the
minority costume image dataset.
.......................................(13)
be the feature of query image Q and image T in the database. Then the retrieval results can be
returned by computing a similarity measure of feature vector between query image and every image
in the dataset.
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CHAPTER 4
RESULTS AND CONCLUSION
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RESULTS AND CONCLUSION
4.1 Introduction
In other words looks at the set of all image retrieved by the CBIR system and finds out how
many of those images are really relevant to the given query, while the recall looks to the whole
database of the images to find out whether there are some images in the database which are
actually relevant to the query but not retrieved by the CBIR system. In both metrics, one
requires the knowledge of the meaning of relevance, which maybe somehow subjective.
Traditionally results are summarized as precision-recall curve. Research in information
retrieval systems has shown that precision and recall follow an inverse relationship. When
dealing with several queries, one often uses the mean average precision, where precision is
calculated over a number of different queries.
4.2 Simulation results
Here the given image is compared with the image present in the database and we will get the
output
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Table 4.2.1 Average retrival precision and recall in the RGB color space
The quantization
Precision(%)
Recall(%)
number for
colour
12
18
24
30
36
12
18
24
30
36
216
61.7 62.0 62.6
62.5
62.42
7.41
7.45
7.48
7.50
7.49
128
65.1
65
65.7
65.9
65.57
7.82
7.88
7.89
7.91
7.87
64
63.9
64
64.6
64.4
63.85
7.68
7.72
7.76
7.73
7.66
32
61.0
61
62.6
62.2
61.42
7.32
7.43
7.47
7.47
7.37
Table 4.2.2:Average retrival precision and recall in the HSV colour space
The quantization number for
color
Precision(%)
12
18
24
30
Recall(%)
36
12
18
24
30
36
192
60.8 60.96 60.8 61.08 61.6
7.30
7.32
7.30 7.33 7.36
128
63.2 63.2 63.35 63.50 63.5
7.59
7.59
7.60 7.62 7.62
108
61.1 61.5 61.64 61.63 61.65 7.39
7.39
7.40 7.40 7.40
72
62.3 62.9 62.68 62.82 62.68 7.48
7.55
7.52 7.54 7.52
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Table 4.2.3:Average retrival precision and recall in the L*A*B Colour space
The quantization number for
colour
Precision(%)
12
18
24
30
58.9
59.1
Recall(%)
36
12
24
30
36
180
58.4 58.8
7.0 7.07 7.09
7.10
160
55.9 56.99 57.07 57.42 57.22 6.71 6.84 6.85 6.89
6.87
90
53.58 54.74 55.13 55.17 54.65 6.43 6.57 6.62 6.62
6.56
45
52.54 54.29 54.67 54.68 53.89 6.31 6.52 6.56 6.56
6.47
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4.3 Conclusion
This report reviewed the main components of a content based image retrieval system,
including image feature representation, indexing, query processing, and query image matching
and user's interaction, while highlighting the current state of the art and the key-challenges. It
has been acknowledged that it remains much room for potential improvement in the
development of content based image retrieval system due to semantic gap between image
similarity outcome and user's perception. Contributions of soft-computing approaches and
natural language processing methods are especially required to narrow this gap.
Standardization in the spirit of MPEG-7, which includes both feature descriptor and language
annotation for description various entity relationships, is reported as a crucial step.
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APPENDIX
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APPENDIX
A.Software Description
The software used for the implementation of the project was MATLAB. The latest version
of
MATLAB ,R2017a was used. It consists of many tool boxes. Among the many tool boxes, Image
tool processing tool box used for this project .With the help of image processing tool box, many
commands are used in a simple way. It provides all the basic commands which are used for the
implementation of image processing tool projects.
Image processing tool box provides a comprehensive set of reference standard algorithms and
work flow apps for image processing, analysis, visualization and algorithm development. It is a
possible to perform image segmentation, image enhancement, noise reduction ,geometric
transformations ,image registeration. Many operations can be performed using image processing
tool box in MATLAB.
Image processing tool box apps lets you automate image processing workflows .you can
interactively segment image data, compare image registeration techniques ,and batch process large
data sets.
The following are the steps followed during the implementation of the project on MATLAB
R2017a . These are the not the actual steps to be followed depending on the version of the
MATLAB, the changes can be made accordingly. The commands available in the version of R2017
a may not be available in other versions.
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Step 1:
 First of all run the cbires and we will get the GUI interface on which our further operations
will be Perfomed.
Step 2:
 GUI interface will perform the operations like retrival of images.
Ste
p 3:

I
n
G
U
I
i
n
t
erface, we need to load images from the dataset .
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Step 4:
In nextstep, we need to load the images (database ). In this we created a image database of
Different datasets like people, buses, flowers ,animals, buildings etc

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step
5:

erwards,
images are
loaded
succesfull
y.
Step 6 :
 Here we can see the images of different dataset.
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f
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
Here our query image which is nothing but input image which is compared to other images .

Images which are related to content will be displayed.

The number of images we want we can retrive from our database.
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
In this interface we can apply different filters to images and we can observe the difference
between them.
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B.source code
function waveletMoments =waveletTransform(image,spaceColor)
%input :image to process and extract wavelet coefficients from
%output :1*20 feature vector containing the first 2 moments of wavelet
%coefficients
if(strcmp(spacecolor,’truecolor’)==1)
imgGray=double(rgb2gray(image))/255;
imgGray=imresize(imgGray,[256 256]);
elseif (strcmp(spacecolor,’grayscale’)==1)
imgGray=imresize(imgGray,[256 256]);
end
coeff1=dwt2(‘imgGray’,’coif1’);
coeff2=dwt2(coeff1,’coif1’);
coeff3=dwt2(coeff2,’coif1’);
coeff4=dwt2(coeff3,’coif1’);
%construct the feature vector
meanCoeff=mean(coeff4);
stdCoeff=std(coeff4);
WaveletMoments=[meanCoeff stdCoeff];
end
Department of ECE
Page 29
CMRCET
CONTENT BASED IMAGE RETRIVAL THROUGH SEARCHING METHOD
Department of ECE
Page 30
CMRCET
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