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ABSTRACT

Content-Based Image Retrieval (CBIR) is a challenging task available techniques uses only low-

level features. May be different in terms of user perception, our objective is to retrieve images based on

color and boundary. For that, we present a new method for comparing images based on combining

between edge detection using comparison method(mean,nighbour) and the image without change using

comparison method .The goal of this research project is to understand the ideas of image retrieval by

content of image, that can provide a large variety of image-distance measures that can be used singly or

in combination for image features. The focus of the work is the development of new algorithm by

combining more features and algorithms to develop effective method to support fast querying of large

collection of image databases with user-specified distance measures. Methods for merging in general,

distance-measure-independent algorithms with other useful techniques. The problem of providing users

with multiple distance measures of many different varieties is being studied. New methods for

combining distance measures and Edge detection method then combine the result to in parallel way to

improve the result.. A prototype system is being implemented to test the developed methods, and

evaluation is being performed on both a large general image database and a smaller controlled database.

The results of this research will be techniques that facilitate rapid retrieval of images by eliminating

many images from database during the search, making content-based retrieval feasible on very large and

growing databases.

Keywords: CBIR,Image Retrieval, Edge Detection ,Query by image and content based image.

INTRODUCTION

Content-Based Image Retrieval(CBIR),also

known as Query By Image Content(QBIC). It is

application of computer vision to the image

retrieval problem .To search for digital image in

large collection of images or databases for

similar image. "Content-based" mean that the

search will analyze the actual contents of the

content refer to the color or texture or shape or

any other information that can be derived from

The image itself to search for similar images

which contain similar features.[1] CBIR systems

Content Based Image Retrieval Using Edge Detection and Color

Khaled Fayez Meri1, Dr. Said Mohammed AbuSha'ar

2

1Department of computer science, Graduate School of Information Technology, University of Palestine,

Gaza,[email protected],

2Information Technology Faculty, Al-Ahliyya Amman University,

Amman, Jordan [email protected]



demonstrate excellent performance at computing

low-level features from pixel representations but

its output does not reflect the overall desire of

the user. The systems perform poorly in

extracting high-level (semantic) features thatinclude objects and their meanings, actions and

feelings. This phenomenon, referred to as the

semantic gap, has necessitated current research

in CBIR systems towards retrieving images by

the type of object or scene depicted. [2]

Feature Extraction Is one of the most

important components in a content-based

retrieval system. Since a human is usually

judging the results of the query, extracted

features should mimic the human visual

perception as much as possible. In broad sense,

features may be divided into low-level features

(such as color, texture, shape, and spatial layout)

and high-level semantics (such as concepts and

keywords). Use of only low-level features might

not always give satisfactory results, and

therefore, high-level semantics should be added

to improve the query whenever possible. High-

level semantics can be either annotated

manually or constructed automatically from

low-level featuresImage Histogram An image histogram is a

histogram of the values of the pixels in a digital

image. Image editors have provisions to create

an image histogram of the image being edited.

The histogram plots the number of pixels in the

image (vertical axis) with a particular brightness

value (horizontal axis). Algorithms in the digital

editor allow the user to visually adjust the

brightness value of each pixel and to

dynamically display the results as adjustments

are made Improvements in picture brightnessand contrast can thus be obtained[55].

Edge Detection is one of the most important

tasks in image processing and scene analysis

systems and computer vision . particularly in

within the areas of feature detection and feature

extraction, to refer to algorithms which aim at

identifying points in a digital image at which the

image brightness changes sharply or more

formally has discontinuities. It denotes the

procedure of detecting meaningful

discontinuities (edges) of the image function.

The accuracy in detecting these discontinuities

and efficiency in implementing these operations

are important criteria for using an algorithm[65].

Edges in gray-level images can be thought of as

pixel locations of abrupt gray-level change. Achange in the image function can be described

by a gradient that points in the direction of the

largest growth of the image fuction. Therefore,

one edge detection technique is to measure the

gradient vector magnitude at pixel locations.

Fundamental difference between color images

and gray-level image is that, in a color image, a

color vector which generally consists of three

components is assigned to a pixel, while a scalar

gray-level is assigned to a pixel of a gray-level

image. Thus, in color image processing vector-

valued image functions are treated instead of

scalar image functions. The techniques used for

this can be subdivided on the basis of their

principle procedures into two classes.[60]

Monochromatic-based techniques: methods in

this category treat information from the

individual color channels or color vector

components first separately and then combine

together the individual results gained.

Vector-valued techniques: vector-valuedtechniques treat the color information as color

vectors in a vector space provided with a vector

norm.

Edges are commonly defined as significant local

changes in an image. Edge provides an

indication of the physical extent of objects in the

image. Edge detection is viewed as an

information reduction process that provides

boundary information of regions by filtering out

unnecessary information for the next steps of

processes in a computer vision system. Thus, edge detection is one of the most essential steps

for extracting structural features for human and

machine perception. The success of high-level

computer vision processes heavily relies on the

good output from the lower level processes such

as edge detection. Many edge detection

algorithms have been proposed in the last 50

years.[60]

The Sobel operator is used in image

processing, particularly within edge detection



user

Image DB

FeatureExtraction

Results

SimilarityCalculation

Figure 2 : Images with similar features are retrieved

User query

algorithms. Technically, it is a discrete

differentiation operator, computing an

approximation of the gradient of the image

intensity function. At each point in the image,

the result of the Sobel operator is either thecorresponding gradient vector or the norm of

this vector. The Sobel operator is based on

convolving the image with a small, separable,

and integer valued filter in horizontal and

vertical direction an is therefore relatively

inexpensive in terms of computations. On the

other hand, the gradient approximation which it

produces is relatively crude, in particular for

high frequency variations in the image.[66][24]

Figure1:Sobel operator applied to a color photograph

Experimental DataIn a content based image retrieval system

querying can be done with a query image

(Figure 2). A combination of two algorithms ormore also be used, Then The goal in each case is

to find the images most resembling the query.

To achieve this, a number of different

approaches have been suggested in this thesis.

In this section, we will describe the algorithms

which used and The collection which used to

test the methods. In our research use two data

collections ,first collection consist of 70 images

(human, dogs apples,cars,home,etc..) and the

second collection consist of 600 images divide

into six group every group consist of 100 imagesfrom the same kind (dinosaurs , flowers, horses,

mountains, elephants, buses) was used as a test

for Image query to search inside Figure 3 , The

images which retrieved under our conditions

which specify in our research will be consider a

retrieved images, The retrieved images consist

of relevant images and irrelevant images .

In this section, we will describe the algorithms

which used and The collection which used to

test the methods. In our research use two data

collections ,first collection consist of 70 images

( apples,cars,home,etc..) and the second

collection consist of 600 images divide into six

group every group consist of 100 images from

the same kind (dinosaurs , flowers, horses,

mountains, elephants, buses) was used as a test

for Image query to search inside . The images

which retrieved under our conditions which

specify in our research will be consider a

retrieved images, The retrieved images consist

of relevant images and irrelevant images .

Query database

Figure 3 ; CBIR System



COMPARISON ROUTINES

A histogram is one way of representing the

colour content of an image, and so the similarity

of two images can be determined by comparingtheir histograms. The main design decision on

histograms was made early on in the project. It

was decided to split an image up into several

sub-images (by placing an image up to 4x4 grid

over the image) which used in neighbour

Method and then a histogram would be

produced for each sub-image. Comparison of

two images would involve comparing respective

histograms and summing the results. The

splitting of the image into sub-images has been

seen, during implementation, to makecomparisons more effective. The histograms of

the sub-images were eventually implemented as

Histogram objects, with the content of the whole

image being represented in an Image Histogram

object (which stores an array of sub-image

Histograms). Each Histogram is a three-

dimensional array of integers with the axes

representing quantized amounts of red, green

and blue respectively. Each element in the array

therefore corresponds to a collection of colours

with similar red, green and blue values.

Compares the current Image Histogram to other

Image histograms values using the comparison

by means and neighbour method together

between the two Histograms. Values close to

zero imply that the two Histograms are very

similar according to this method.

The First stage we use the original image to test

in the collection by using the Euclidean distance

using Image histogram value to compare

between the values. We use also the Neighbourcomparison method – The neighbour

comparison method is distinctly different from

the mean comparison method, in that it performs

comparisons within the image before comparing

it to another image. The neighbour array stores

the results of these comparisons, and shows how

similar sections of the image are to each other.

So, if an image has very little internal colour

variation, then it should have a neighbour array

with values close to 0.0. If, however, the image

has a lot of internal variation in colour, then this

will be reflected in the neighbour array with

numbers closer to 1.0. From this standpoint, the

neighbour comparison method can be though of

as comparing images by their colour complexity.

Whereas methods such as the meansComparison method compares images by how

similar the actual (average) colours are, the

neighbour comparison method is concerned with

the colour change that occurs within an image.

Hence it is possible that two images that are

dissimilar according to the means comparison

method (due to the colours looking different) are

viewed as similar by the neighbour comparison

method due to the fact that they both, for

example, have a gradual darkening of colour

from one corner to another.

We specify the ratio of similarity up to 85% for

retrieved images in this range not less than this

level( in colour group).

All the comparison methods suffer from one key

problem, and that is the histograms are not

unique. This problem is summed up from the

following quote: Although a histogram gives us

the frequency distribution of [grey] levels in an

image, it can tell us nothing about the way in

which [grey] levels are distributed spatially.In other words, the same histogram can come

from many different images. This can obviously

lead to problems: a computer may calculate that

two images are very similar, yet to a human

observer they may be completely different

(although they will obviously have some colour

similarities). This is one of the major limitations

of a content analysis based solely on colour

distribution and the histogram technique.

Implementation of the comparison methods

Most of the comparison methods werereasonably simple to implement in an efficient

manner.

This is due to the fact that the neighbour

comparison method compares based on how

colour varies within an image, rather than

comparing the actual colours in the image.

Hence, an all black image will be identical to an

all white image using neighbour comparison,

but will be returned as completely different

using the means comparison method. but the

means comparison method gave results that



were more intuitive from a human point of view.

Overall, the comparison of images based on

colour distribution produces reasonably good

results. However, the user needs to be aware of

the limitations of the methods. Also, knowledgeof the comparison methods helps to interpret the

order of the results.

The results that we have obtained by

implementing this project have served to

illustrate the complexity of image recognition

which, being an everyday operation for the

human brain, is extremely complicated to

accurately recreate within a computer. The main

way to improve upon the program that has been

produced is to investigate and implement more

powerful comparison techniques, using ideas

such as shape and other teqniques.

Figure 4:Different histogram,

same shape, very similar to a human

But as we know same histogram can come from

many different images. And some different

histogram can come from the same images but

different in colour as Figure 4 so This can

obviously lead us to problems: a computer may

calculate that two images are very similar, yet to

a human observer they may be completely

different (although they will obviously have

some colour similarities). This is one of the

major limitations of a content analysis based

solely on colour distribution and the histogram

technique. So we will solve this by using the

second stage which we added it in parallel wayto the first stage to determine the edge of the

object inside the image as method to determine

the boundary which lead us to the shape we

focus on the idea that edges define boundaries

and that regions are contained within these

edges. The algorithm used in this program goes

as follows:

In second stage we will use Edge Detection

Methods we convert the same collection to

Grayscale image using the Sobel edge

detections. We use this collection in our

program by using the same methods which was

the neighbor and Mean methods so we can get

new collection of retrieved images and we

specify the ratio of similarity 97% for retrievedimages in this range not less than this level ,but

in small group we decrease the group of

retrieved images by increase the ratio to 98 –

98.5 of similarity

Finally we compare the two retrieved

collections together and we select the image_id

which duplicated in the two collections which

retrieved twice by the two stages and this

selected group will be consists of (relevant

image and irrelevant images) . The image

selected with its rank number in color image

retrieved group and it is rank number in sobel

retrieved group. Then we add the two ranks

together .Then we sort the entire selected group

by summed rank number ascending so the little

rank will be the first candidate in retrieved

group. After that we will classify the retrieved

group as relevant or irrelevant.

In our test we use the first collection which

consist of 70 images we searched by the content

of the apple image on the other images and wespecify the ratio of retrieved image to be

Euclidean distance are typically used to

determine similarities between a pair of images

Figure 5, but the same histogram can come from

many different images. This can obviously lead

to problems: a computer may calculate that two

images are very similar, yet to a human observer

they may be completely different (although they

will obviously have some colour similarities).

This is one of the major limitations of CBIR

system. Also we use the collection which consistof 600 images Figure 6, Figure 7 example of

rose and horse query here we retrieved just

images more or equal 85% similarity in colour

,but in second group we use edge detection by

converting every images in collection to

grayscale and then the retrieved images will be

97% similar or up to 100% .



Query Image

Image Retrieved

Figure5 :Apply Retrieved Group



Query Image

Image Retrieved

Figure 6 :Flower Retrieved Group



Query Image

Image Retrieved

Figure 7 :Horse Retrieved Group



Dinasour Example

The collection consist of 100 image of dinosaur and 500 images consist of ( flowers, horses, mountains,

elephants, buses) in our example we use to search in the collection until 85% similarity .

Query Image

Retrieved Image

Figure٨.1: retrived group1 Figure٨.2: retrived group2



Figure 8.11: retrived group11

Using original image in Query

No of Relevant Image 100

No of Image retrived 270

precision ratio 37.04%

Table1 :Precision rate using original image

By this method we get all the relevant images but we also retrieved 170 images irrelevant so we need to

do the next step by using sobel method and convert all the collection of 600 images to grayscale.



The collection consist of 100 image of dinosaur and 500 images consist of ( flowers, horses, mountains,

elephants, buses) in our example we use to search in the collection until 97.5% similarity .

Figure 8.1: retrived group1 Figure 8.2: retrived group2

Figure 8.3: retrived group3 Figure 8.4: retrived group4



Figure 8.5 : retrived group 5 Figure 8.6 : retrived group 6

Figure 8.7 : retrived group 7 Figure 8.8: retrived group 8

using Sobel image in Query

Relevant Image 83

Image retrived 175

precsicion ratio 47.43%

Table 2 :Precision rate using sobel image

By this method we do not get the all relevant images but we also retrieved 175 images so we decrease

the number of image retrieved so the irrelevant images will be 92 .If we notice the irrelevant images in

the sobel method are not all of them similar to the irrelevant images in the first step.



Intersection (set theory)

Figure 7.9: Intersection of set A and B

In mathematics, the intersection of two sets A

and B is the set that contains all elements of A

that also belong to B (or equivalently, all

elements of B that also belong to A), but no

other elements.[67]

The intersection of A and B is written " A ∩ B".

Formally:

x is an element of A ∩ B if and only if

x is an element of A and

x is an element of B.

For example:

• The intersection of the sets {1, 2, 3}

and {2, 3, 4} is {2, 3}.

So we here apply the intersection theory

between the two sets (two retrieved groups ) thefirst set is the collection of retrieved image by

original image and the second set is the retrieved

collection by edge detection .then we select the

intersection area (duplicate images).every image

selected with her rank in every group then we

add the two ranks together then the sum of the

two rank is will be the final rank for the image

in the final retrieved group.[67]

The result table3 By this method we get

precision rate to reach up to 93

Using IntersectionRelevant Image 83

Image retrived 89

precsicion ratio 93.26%

Table3: Precision rate using intersection

CBIR System software

We use java language in CBIR system, The

database files was stored as plain text and there

will not be a limit on the size of the database or

any databases added to the program as long as

there is storage space to accommodate them.

The program will also allow new databases to be

created by the user, with any images provided by

the user to be added into them. The database file

itself will contain information about an image

including its location, and possibly other image

information such as the histogram data. Some

algorithms doing by mathlab or by alone java

code like edge detection to convert all of

collection to image gray scale.

Algorithms' Pseudo Code

1. Input Query image

2. Get color histogram for image

3. Calculate the mean histogram and neighbor

histogram for query image.

4. for i=1 to n

Calculate mean histogram and neighbor

histogram for N images

5. for i= 1 to n



If(histogram[query_image]-histogram[i]>=0.85)

then

Image_id_percent[i]=100*

(histogram[query_image]-histogram[i])

Else nothing

6.sort For each image_id_percent[1..n]

7.rank(color) every image depend on similarity

8.convert every image in collection to gray scale

using edge detection algotithm(sobel)

9. input Query image (gray scale)

10.get edge histogram for images

11. Calculate the mean histogram and neighbor

histogram for query image.

12. for i= 1 to n

If(histogram[query_image]-histogram[i]>=0.98)

then

Image_id_edge__percent[i]=100*

(histogram[query_image]-histogram[i])

Else nothing

13.sort For each image_id__edge_percent[1..n]

14.rank(edge_image) every image depend on

similarity

15.do intersection between the two retrieved

collection (edge_images and color_images)

16.rank_intersection = rank(edge_image)+

rank(color)

17. Get retrieved images

End.

Conclusions and Future Work

The choice of a feature selection method plays a

critical role in the success of a versatile CBIR

system The main contribution of this thesis is

the design of a new application with new

methodology to generate retrieved images for an

image content And decrease the number of

irrelevant images. The system use options for

feature selection and extraction feature

combination and weighting, and similarity

measures, all available through a GUI We have

also introduced a metric for ranking the results,

based on the comparison of histogram images

between a query image and the image collection.

In Future we intend to be more specific in type

of images that mean to be more specific with

some methodology and algorithms, and trying to

decrease the number of irrelevant of retrieved

group.

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الحدود واأللواناكتشافاسترجاع الصورة عن طريق محتوياتها باستخدام

٢ شعرأبو سعيد محمد. ،د١ خالد فايز ميري.أ١ةعماج نيطسلف ،، ايلعلا تاساردلا مسق يجولونكت ا تامولعملانيطسلف ، ةزغ،. ٢ةعماج ، ةيلهألا نامعةيلك ، نامع ، تامولعملا ايجولونكت ندرألا

ملخصال

الصور عـن طريـق وانطالقا من محتوى استرجاع صعبة مهمةاسترجاع الصور عن طريق محتوياتها هيإن

هدفنا فـي. منخفض السماتال مستوىال تستخدم التقنيات المتاحة فقط علىهي مهمة صعبة و) )CBIRمحتوياتها

الـدمج ولذلك قدمنا منهجية المقارنة بين الصور باستخدامالبحث هو استخدام البحث عن طريق األلوان والحدود

ط وسـ ال ك وتطبيق الخوارزميـات المعروفـة فـي المقارنـاتاأللواناكتشاف الحدود والمقارنة باستخدام بين

التي يمكن أن توفر مجموعة كبيرة من الصور وعلى الصور األصلية والصور بعد تغييرها.الجوار وخوارزمية

هـو تطـوير تركيزنا في هذا البحث. لمسافات التدابير التي يمكن استخدامها منفردة أو مجتمعة لمالمح الصورة

لتطوير طريقة فعالة لدعم االستعالمالمقارنة خوارزمية جديدة عن طريق الجمع بين أكثر السمات وخوارزميات

تطبيق لالتدابير مسافة محددة من باستخدام مجموعة من الصور مع قواعد البيانات للمستخدمين بين بسرعة كبيرة

ثم تطبيقها على الصور بعد تحويلهـا باسـتخدام حتى نسبة معينة ومنالخوارزمية على الصور الملونة كما هي

الوسط والجوار على الصور المحولة ومن ثم اسـتخدام خوارزميـة التقـاطع بـيناستخدام و خوارزمية سوبل

تم تطبيق نظـام الختبـار األسـاليب. وذلك لتحسين النتائجالمجموعتين وذلك لحذف الصور الغير مرغوب بها

الصور الصغيرةعلى مجموعة قاعدة بيانات وأيضاالمطورة وتقييمها على مجموعة قاعدة بيانات الصور الكبيرة

استبعاد الصور غير المرغوب بها فـي نتائج هذا البحث ستكون تقنيات تسهل سرعة استرجاع الصور من خالل

.على قاعدة البيانات المتنامية يمكن تنفيذهالبحث داخل قاعدة البيانات مما يجعل البحث

رة،اكتشاف الحافة ،االستعالم بالصورةاسترجاع الصورة عن طريق محتوياتها ،استرجاع الصو:الكلمات الدالة

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