sbir

B.Tech Project Report

SKETCH4MATCH-CONTENT BASED IMAGE

RETRIEVAL

Submitted in partial fulfilment for the award of the Degree of

Bachelor of Technology in Information Technology

Submitted by

Ms. GEETHU P T (Roll No. 14112413)

Ms. HAFSA HASSAN (Roll No. 14112414)

Ms. HONEY MERRIN SAM (Roll No. 14112415)

Ms. SHIBIJA K (Roll No. 14112419)

Under the guidance of

Mrs. Shahanaz N

Department of Information Technology

CO-OPERATIVE INSTITUTE OF TECHNOLOGY, VATAKARA

(Engineering College under CAPE, Estd by Govt. of Kerala)

APRIL 2014

CERTIFICATE

This is to certify that the project entitled SKETCH BASED IMAGE

RETRIEVAL is the bonafide record of the major project done by Ms.

GEETHU P T (Roll no.14112413), Ms. HAFSA HASSAN (Roll

no.14112414), Ms. HONEY MERRIN SAM (Roll no.14112415), Ms.

SHIBIJA K (Roll no.14112426) under my supervision and guidance, in

partial fulfilment for the award of Degree of Bachelor of Technology in

Information Technology from Cochin University of Science And

Technology for the year 2014.

Mrs. SHAHANAZ N

(Guide)

Asst. Professor


Mrs. NITHYA A.K.

Professor and Head


ACKNOWLEDGEMENT

First and foremost, we wish to place on records our ardent and earnest gratitude to our

beloved principal Dr. O A JOSEPH.

It is a matter of great pleasure for us to express our sincere gratitude and appreciation

to our beloved project guide Mrs. SHAHANAZ N, Assistant Professor, Dept. of

Information Technology. Her tutelage and guidance was the leading factor in

translating our efforts to fruition. Her prudent and perspective vision has shown light

on our trail to triumph.

We are extremely happy to mention a great word of gratitude to Mrs. NITHYA

A.K., Head of the Department of Dept. of Information Technology for providing us

with all facilities for the completion of this work.

Finally yet importantly, we would like to express our gratitude to our project

coordinator Mr. SIDHIK A for his valuable assistance provided during the course of

the project.

We would also extend our gratefulness to all the staff members in the Department.

We are also thankful to our friends and well wishers for sharing their knowledge and

suggestion.

GEETHU P T

HAFSA HASSAN

HONEY MERRIN SAM

SHIBIJA K

ABSTRACT

The content based image retrieval (CBIR) is one of the most popular, rising research

areas of the digital image processing. Most of the available image search tools, such

as Google images and Yahoo! Image search, are based on textual annotation of

images. In these tools, images are manually annotated with key words and then

retrieved using text-based search methods. The performances of these systems are not

satisfactory.

The goal of CBIR is to extract visual content of an image automatically, like color,

texture, or shape.

This project aims to introduce the problems and challenges concerned with the design

and the creation of CBIR systems, which is based on a free hand sketch (sketch based

image retrieval-SBIR). With the help of the existing method, describe a possible

solution how to design and implement a task specific descriptor, which can handle the

information gap between a sketch and a colored image, making an opportunity for the

efficient search hereby. The used descriptor constructed after such special sequence of

preprocessing steps that the transformed full colored image and the sketch can be

compared.

The SBIR technology can be used in several applications such as digital libraries,

crime prevention, and photo sharing sites.

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CONTENTS

Chapter No TITLE Page No

List of Abbreviations v

List of Figures vi

1 INTRODUCTION 1

1.1 General 1

1.1.1 The Definition of Image 2

1.1.2 Image Processing 2

1.1.3 Image Retrieval and Information Retrieval 3

1.2 Content Based Image Retrieval 4

1.3 Content Based Image Retrieval Using Color 5

1.4 Existing System 7

1.4.1 Automatic Image Annotation and

Retrieval using Cross Media

Relevance Models 7

1.4.2 Concept Based Query Expansion 8

1.4.3 Query System Bridging the Semantic Gap

For Large Image Databases 8

1.4.4 Ontology Based Query Expansion Widget

For Information Retrieval 9

1.4.5 Detecting Image Purpose in World-Wide

Web Documents 9

1.4 Proposed System 9

ii

1.5 Objective 10

1.6 Applications 10

1.7 Literature Survey 11

1.8 Research Challenges 12

2 PROJECT DESCRIPTION 13

2.1 Introduction 13

2.2 Main Modules 13

2.2.1 Index 13

2.2.2 The K-means Algorithm 14

2.2.3 Searching 15

2.3 Performance Matrices 16

3 REQUIREMENT ENGINEERING 17

3.1 Hardware Requirements 17

3.2 Software Requirements 17

3.3 Functional Requirements 18

3.4 Non-Functional Requirements 18

4 ALGORITHMS DESCRIPTION 19

4.1 Color Layout 19

4.2 Color Spaces 19

4.2.1 RGB 19

4.2.2 HSB 20

4.3 Histograms 20

4.3.1 Cumulative Color Histogram 20

4.3.2 Color Set 20

4.3.3 Color Descriptor 21

4.3.4 Edge Histogram 21

iii

4.3.5 Dominant Color Descriptor 22

4.3.6 HSV Color Model 22

4.3.7 Color Co-occurrence Matrix 23

5 TESTING 24

5.1 Introduction 24

5.2 Implementation 24

5.3 Testing 25

5.3.1 System Testing 25

5.3.2 Module Testing 25

5.3.3 Integration Testing 26

5.3.4 Acceptance Testing 26

6 SYSTEM DESIGN 27

6.1 Activity Diagram 27

6.2 Use Case Diagram 29

6.3 Data Flow Diagram 30

6.4 Sequence Diagram 31

7 GLOBAL STRUCTURE AND SUBSYSTEM 33

7.1 Global Structure of a System 33

8 DEVELOPMENT TOOLS 35

8.1 General 35

8.2 Features of JAVA 35

8.2.1 The JAVA Framework 35

8.2.2 Objectives of JAVA 36

9 IMPLEMENTAION 38

9.1 General 38

9.2 Implementation of SBIR System 38

iv

9.2.1 Image Comparison 38

10 SNAPSHOTS 46

11 CONCLUSION 51

12 FUTURE ENHANCEMENT 52

13 REFERENCES 53

v

List of Abbreviations

CBIR - Content Based Image Retrieval

RF - Relevance Feedback

SBIR - Sketch Based Image Retrieval

QBIC - Query By Image Content

SVM - Support Vector Machine

JVM - Java Virtual Machine

GUI - Graphical User Interface

AWT - Abstract Window Toolkit

SWT - Standard Widget Toolkit

PWT - Pyramidal Wavelet Transform

HSV - Hue, Saturation, Value

GCH - Global Color histogram

LCH - Local Color Histogram

RGB - Red, Green, Blue

JPEG - Joint Photographic Expert Group

vi

List of Figures

Figure No TITLE Page No

2.1 Working of SBIR system 15

4.1 Five types of Edge Histogram 22

4.2 SBIR using HSV Color Model 23

6.1 Activity Diagram 28

6.2 Use Case Diagram of Admin 29

6.3 Use Case Diagram of User 30

6.4 Data Flow Diagram 31

6.5 Sequence Diagram 32

7.1 Same Image of Different Contrast 33

7.2 Global Structure of System 34

7.3 System for Database containing Simple Images 34

10.1 Login Frame 46

10.2 Admin Home 47

10.3 Add Image 47

10.4 View Image 48

10.5 Delete Image 48

10.6 User Home_1 49

10.7 User Home_2 49

10.8 Paint with Off-Screen Canvas 50

B.Tech Information Technology, CUSAT SKETCH BASED IMAGE RETRIEVAL

1

CHAPTER 1

INTRODUCTION

1.1 GENERAL

The Sketch Based Image Retrieval (SBIR) is one of the most popular, rising

research areas of the digital image processing. Most of the available image

search tools, such as Google Images and Yahoo! Image search, are based on

textual annotation of images. In these tools, images are manually annotated

with keywords and then retrieved using text-based search methods. The

performances of these systems are not satisfactory. The goal of SBIR is to

extract visual content of an image automatically, like color, texture, or shape.

This paper aims to introduce the problems and challenges concerned with the

design and the creation of SBIR systems, which is based on a free hand sketch.

With the help of the existing methods, describe a possible solution how to

design and implement a task specific descriptor, which can handle the

informational gap between a sketch and a colored image, making an

opportunity for the efficient search hereby. The used descriptor is constructed

after such special sequence of preprocessing steps that the transformed full

color image and the sketch can be compared. Experimental results on two

sample databases showed good results. Overall, the results show that the

sketch based system allows users an intuitive access to search-tools. The SBIR

technology can be used in several applications such as digital libraries, crime

prevention, and photo sharing sites. Such a system has great value in

apprehending suspects and identifying victims in forensics and law

enforcement. A possible application is matching a forensic sketch to a gallery

of mug shot images. The area of retrieve images


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based on the visual content of the query picture intensified recently, which

demands on the quite wide methodology spectrum on the area of the image

processing. The CBIR systems have a big significance in the criminal

investigation. The identification of unsubstantial images, tattoos and graffitis

can be supported by these systems. Similar applications are implemented.

Another possible application area of sketch based information retrieval is the

searching of analog circuit graphs from a big database. A possible matching a

forensic sketch to gallery of mug shot images. The area of retrieve images

based on the visual content of the query picture intensified recently, which

demands on the quite wide methodology spectrum on the area of the image

processing.

1.1.1 DEFINITION OF IMAGE

Data represents a two-dimensional scene. A digital image is composed of

pixels arranged in a rectangular array with a certain height and width. Each

pixel may consist of one or more bits of information, representing the

brightness of the image at that point and possibly including color information

encoded as RGB triples. Picture a visual representation (of an object or scene

or person or abstraction) produced on a surface, "they showed us the pictures

of their wedding" a movie is a series of images projected so rapidly that the

eye integrates them.

Effective indexing and retrieving desired image in large image database in the

basis of features such as color, text and shape can be automatically extracted

from the images themselves.

1.1.2 IMAGE PROCESSING

The analysis of a picture using techniques can identify shades, colors and

relationships that cannot be perceived by the human eye. Image processing is

used to solve identification problems, such as in forensic medicine or in

creating weather maps from satellite pictures. It deals with images in

bitmapped graphics format that have been scanned in or captured with digital


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cameras. Any image improvement, such as refining a picture in a paint

program that has been scanned or entered from a video source.

1.1.3 IMAGE RETRIEVAL AND INFORMATION RETRIEVAL

Since the 1970s Image Retrieval has become a very active research topic, with

two major research communities, database management and computer vision.

One is text-based and another is visual-based. Text-based image retrieval has

become very popular since 1970s, which involves annotating the image with

keywords, and use text-based database management systems (DBMS) to

retrieve the images. In text-based image retrieval system, keywords of

semantic information are attached to the images.

They can be typed manually or by extracting the captions of the images. It is

very efficient for simple and small image databases, since the whole database

can be described by just few hundreds of keywords. But in the 1990s, several

large leaps in development of processor, memory and storage made the size of

image databases grow dramatically. As the image database and image size

grow, there will be more images having different contents and the images

having rich contents cannot be described by only several Semantic keywords.

The demand of labor on annotating the images also rises dramatically.

Retrieval image provides effective and efficient tool querying large image

database. Information retrieval provides the textual representation of images.

It requires the text descriptions to the respective images.

Recent technology development in various fields has made large digital image

databases practical. Well organized database and efficient browsing, storing,

and retrieval algorithms are very important in such systems. Image retrieval

techniques were developed to aid these components.

Image Retrieval was originated from Information Retrieval, which has been

very active research topic since 1940s. We have huge amounts of information

to which accurate and speedy access is becoming ever more difficult. In

principle, Information Retrieval is simple. It can be illustrated by a scene of a


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store of documents and a person (user of the store). He formulates a question

to which the answer is a set of documents satisfying his question. He can

obtain the set by reading all the documents in the store, retaining the relevant

documents and discarding all the others. In this scene, it is a perfect retrieval.

But in practice, we need to model the read process in both syntactic and

semantic to extract useful information. The target of Information Retrieval is

not only how to extract useful information, but also how to measure

relevance among documents. These challenges also exist in Image Retrieval.

Also the keywords are very dependent on the observers interest and they are

subjective. Captions are not always precisely describing the picture.

Indexing and searching a large image database via keywords are time-

consuming and inefficient. Content Based Image Retrieval (CBIR) researches

attempt to automate such complex process of retrieving images that are similar

to the reference image or descriptions given.

1.2 CONTENT BASED IMAGE RETRIEVAL

Content-based image retrieval also known as query by image content and

content-based visual information retrieval problem of searching for digital

images in large database. Content-based means that the search will analyze the

actual contents of image. The term content in this context might refer to

colors, shapes, textures or any other information that can be derived from the

image itself.

The earliest use of the term Content Based Image Retrieval in the literature

seems to be by Kato, was to describe his experiments in automatic retrieval of

images from a database by color and shape features. The term has since been

widely used to describe the process of retrieving desired images from a large

collection on the basis of features (such as color,Texture and shape) that can

be automatically extracted from the images themselves. The features used for


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retrieval can be either primitive or semantic, but the extraction process must

be predominantly automatic.

The ideal approach of querying an image database is using content semantics,

which applies the human understanding about image. Unfortunately,

extracting the semantic information in an image efficiently and accurately is

still a question. Even with the most advanced implementation of computer

vision, it is still not easy to identify an image of horses on a road. So, using

low level features instead of semantics is still a more practical way. Until

semantic extraction can be done automatically and accurately, image retrieval

systems cannot be expected to find all correct images. They should select the

most similar images to let the user choose the desired images. The number of

images of retrieved set can be reduced by applying similarity measure that

measures the perceptual similarity.

A typical CBIR system consists of three major components and the variations

of them depend on features used.

i. Feature extraction Analyze raw image data to extract feature specific

information.

ii. Feature storage Provide efficient storage for the extracted

information, also help to improve searching speed.

iii. Similarity measure Measure the difference between images for

determining the relevance between images.

1.3 CONTENT BASED IMAGE RETRIEVAL USING COLOR

Retrieving image based on color similarity is achieved by computing a color

histogram for each image that identifies the proportion of pixels within an

image holding specific values. Current research is attempting to segment color

proportion by region and by spatial relationship among several color region.


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Among different types of low level features, color is the most straightforward

information which can be easily retrieved from digital images with simple and

compact description, while others require more pre-processing and

computational tasks such as pattern recognition or texture analysis.

While comparing image by color feature, three properties are usually

considered:

i. Area of matching Count the area or number of pixels having same or

similar colors. Larger matched area means more similar.

ii. Color distance Distance between colors, usually in a perceptually

uniform color space. Closer between matched colors means more

similar.

iii. Spatial distribution Usually used while combining color with other

features such as texture and shape.

In a typical color similarity measure, area of matching is usually counted as

the similarity color distance is used to control the matching between colors

and to adjust the similarity. In conventional color image retrieval system, the

most straight forward approach is using color histogram. Histograms of each

color, for example, images of 256 colors, will be generated. Similarities

between such images are then performed and measured by Histogram

Intersection Method (HIM). This is the basic approach and can give simple

and efficient representation of color distribution. Histogram approach is not

limited by taking the number of pixels of each color in the image or using

HIM similarity measure. Indexes of histograms can represent many types of

features such as colors in different color space, coefficients in transformed

domain or spatial-related information. There are also many variations in

comparing histograms. But histograms have a limitation that the feature space

is fixed, compactness of the description is limited, because histograms will not

skip non-existed colors. One argument that we can use a lower resolution

histogram to improve the compactness but it is a trade off between

compactness and accuracy.


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1.4 EXISTING SYSTEM

In earlier days, image retrieving from large image database can be done by

following ways. We will discuss briefly about the image retrieving of various

steps

Automatic Image Annotation and Retrieval using Cross Media

Relevance Models

Concept Based Query Expansion

Query System Bridging The Semantic Gap For Large Image Databases

Ontology-Based Query Expansion Widget for information Retrieval

Detecting image purpose in World-Wide Web documents

Existing system uses a keyword to search an image. Suppose we have to

search for an image of an apple, keyword should be apple and images

related to apple will be displayed accordingly. These images are stored in a

database and are displayed according to the keywords.

1.4.1 Automatic Image Annotation and Retrieval using Cross Media

Relevance Models

Libraries have traditionally used manual image annotation for indexing and

then later retrieving their image collections. However, manual image

annotation is an expensive and labor intensive procedure and hence there has

been great interest in coming up with automatic ways to retrieve images based

on content. Here, we propose an automatic approach to annotating and

retrieving images based on a training set of images.

We assume that regions in an image can be described using a small vocabulary

of blobs. Blobs are generated from image features using clustering. Given a

training set of images with annotations, we show that probabilistic models

allow us to predict the probability of generating a word given the blobs in an

image. This may be used to automatically annotate and retrieve images given a

word as a query. We show that relevance models. Allow us to derive these

probabilities in a natural way. Experiments show that the annotation

performance of this cross-media relevance model is almost six times as good


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(in terms of mean precision) than a model based on word-blob co-occurrence

model and twice as good as a state of the art model derived from machine

translation. Our approach shows the usefulness of using formal information

retrieval models for the task of image annotation and retrieval.

1.4.2 Concept Based Query Expansion

Query expansion methods have been studied for a long time - with debatable

success in many instances. In this project we present a probabilistic query

expansion model based on a similarity thesaurus which was constructed

automatically. A similarity thesaurus reflects domain knowledge about the

particular collection from which it is constructed. We address the two

important issues with query expansion: the selection and the weighting of

additional search terms. In contrast to earlier methods, our queries are

expanded by adding those terms that are most similar to the concept of the

query, rather than selecting terms that are similar to the query terms.

Our experiments show that this kind of query expansion results in a notable

improvement in the retrieval effectiveness when measured using both recall-

precision and usefulness.

1.4.3 Query System Bridging The Semantic Gap For Large Image

Databases

We propose a novel system called HISA for organizing very large image

databases. HISA implements the first known data structure to capture both the

ontological knowledge and visual features for effective and efficient retrieval

of images by either keywords, image examples, or both. HISA employs

automatic image annotation technique, ontology analysis and statistical

analysis of domain knowledge to precompiled the data structure [7]. Using

these techniques, HISA is able to bridge the gap between the image semantics

and the visual features, therefore providing more user-friendly and high

performance queries. We demonstrate the novel data structure employed by

HISA, the query algorithms, and the pre-computation process.


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1.4.4 Ontology-Based Query Expansion Widget for information

Retrieval

In this project we present an ontology-based query expansion widget which

utilizes the ontology published in the ONKI Ontology Service. The widget can

be integrated into a web page, e.g. a search system of a museum catalogue,

enhancing the page by providing query expansion functionality. We have

tested the system with general, domain specific and spatiotemporal ontology.

1.4.5 Detecting image purpose in World-Wide Web documents

The number of World-Wide Web (WWW) documents available to users of the

Internet is growing at an incredible rate. Therefore, it is becoming increasingly

important to develop systems that aid users in searching, altering, and

retrieving information from the Internet. Currently, only a few prototype

systems catalog and index images in Web documents. To greatly improve the

cataloging and indexing of images on the Web, we have developed a prototype

rule-based system that detects the content images in Web documents. Content

images are images that are associated with the main content of Web

documents, as opposed to a multitude of other images that exist in Web

documents for different purposes, such as decorative, advertisement and logo

images. We present a system that uses decision tree learning for automated

rule induction for the content image detection system. The system uses visual

features, text-related features and the document context of images in concert

for fast and effective content image detection in Web documents.

1.4 PROPOSED SYSTEM

Relevance feedback is an interactive process that starts with normal SBIR. The

user input a query, and then the system extracts the image feature and measure

the distance with images in the database. An initial retrieval list is then


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generated. User can choose the relevant image to further refine the query, and

this process can be iterated many times until the user find the desired images.

In many cases if we want to search efficiently some data have to be recalled.

The human is able to recall visual information more easily using for example

the shape of an object, or arrangement of colors and objects. Since human are

visual type, we look for images using other images, and follow this approach

also at the categorizing. In this case we search using some features of images,

and these features are the keywords. Our purpose is to develop a content based

image retrieval system, which can retrieve using sketches in frequently used

databases.

1.5 OBJECTIVE

The goal of SBIR is to extract visual content of an image automatically, like

color, texture, or shape. This project aims to introduce the problems and

challenges concerned with the design and the creation of SBIR systems, which

is based on a free Database oriented Image retrieval System. With the help of

the existing methods, describe a possible solution how to design and

implement a task specific descriptor, which can handle the informational gap

between a source and images in a Database, making an opportunity for the

efficient search hereby.

1.6 APPLICATIONS

Image Database Search System.

Online Search Engines.

Hospital Database Retrieval Systems.

Police Information Database Search Systems.


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1.7 LITERATURE SURVEY

SBIR systems are based on a free hand sketch. With the help of the existing

methods, describe possible solution how to design and implement a task

spastic descriptor, which can handle the informational gap between a sketch

and a colored image, making an opportunity for the efficient search hereby.

The used descriptor is constructed after such special sequence of

preprocessing steps that the transformed full color image and the sketch can be

compared. Experimental results on two sample databases showed good results.

Overall, the results show that the sketch based system allows users an intuitive

access to search-tools.

Our purpose is to develop a content based image retrieval system, which can

retrieve using sketches in frequently used databases. The user has a drawing

area where he can draw those sketches, which are the base of the retrieval

method. Using a sketch based system can be very important and efficient in

many areas of the life. In some cases we can recall our minds with the help of

gores or drawing.

In these systems the user draws color sketches and blobs on the drawing area.

The images were divided into grids, and the color and texture features were

determined in these grids. The applications of grids were also used in other

algorithms, for example in the edge histogram descriptor (EHD) method. The

disadvantage of these methods is that they are not invariant opposite rotation,

scaling and translation. Lately the development of difficult and robust

descriptors was emphasized. Another research approach is the application of

fuzzy logic or neural networks. In these cases the purpose of the investment is

the determination of suitable weights of image features

The SBIR technology can be used in several applications such as digital

libraries, crime prevention, and photo sharing sites. Such a system has great

value in apprehending suspects and indentifying victims in forensics and law

enforcement. A possible application is matching a forensic sketch to a gallery

of mug shot images. The area of retrieve images based on the visual content of

the query picture intensive recently, which demands on the quite wide

methodology spectrum on the area of the image processing.


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1.8 RESEARCH CHALLENGES

The implementation of SBIR systems raises several research challenges. Some

of these are:

Investigating new user interfaces for annotating, browsing, and

searching based on image content.

New tools for marking/annotating images (and their regions)

Matching query and stored images in a way that reflects human

similarity judgments

Providing compact storage for large image databases

Efficiently accessing stored images by content


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CHAPTER 2

PROJECT DESCRIPTION

2.1 INTRODUCTION

Content-based image retrieval (CBIR), also known as query by image content

(QBIC) and content-based visual information retrieval (CBVIR) is the application

of computer vision techniques to the image retrieval problem, that is, the problem of

searching for digital images in large database. Content based image retrieval is

opposed to concept based approaches.

"Content-based" means that the search will analyze the actual contents of the image

rather than the metadata such as keywords, tags, and/or descriptions associated with

the image. The term 'content' in this context might refer to colors, shapes, textures, or

any other information that can be derived from the image itself. CBIR is desirable

because most web based image search engines rely purely on metadata and this

produces a lot of garbage in the results. Also having humans manually enter keywords

for images in a large database can be inefficient, expensive and may not capture every

keyword that describes the image. Thus a system that can filter images based on their

content would provide better indexing and return more accurate results.

2.2 MAIN MODULES

2.2.1 Indexing

Indexing the whole set of images using K-means Clustering algorithm. Indexing is

done using an implementation of the Document Builder Interface. A simple approach

is to use the Document Builder Factory, which creates Document Builder instances

for all available features as well as popular combinations of features In a content


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based image retrieval system, target images are sorted by feature similarities with

respect to the query (CBIR).In this indexing, we propose to use K-means clustering

for the classification of feature set obtained from the histogram. Histogram provides a

set of features for proposed for Content Based Image Retrieval (CBIR). Hence

histogram method further refines the histogram by splitting the pixels in a given

bucket into several classes. Here we compute the similarity for 8 bins and similarity

for 16 bins. Standard histograms, because of their efficiency and insensitivity to small

changes, are widely used for content based image retrieval. But the main disadvantage

of histograms is that many images of different appearances can have similar

histograms because histograms provide coarse characterization of an image.

The k-means algorithm takes the input parameter, k, and partitions a set of n objects

into k clusters so that the resulting intra cluster similarity is high but the inter cluster

similarity is low. Cluster similarity is measured in regard to the mean value of the

objects in a cluster, which can be viewed as the clusters center of gravity. How does

the k-means algorithm work?

The k-means algorithm proceeds as follows. First, it randomly selects k of the objects,

each of which initially represents a cluster mean or center. For each of the remaining

objects, an object is assigned to the cluster to which it is the most similar, based on

the distance between the object and the cluster mean. It then computes the new mean

for each cluster.

2.2.2 The k-means algorithm

Algorithm: k-means. The k-means algorithm for partitioning based on the mean

value of the objects in the cluster.

Input: The number of clusters k and a database containing n objects.

Output: A set of k clusters that minimizes the squared-error criterion.


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Method:

(1) Arbitrarily choose k objects as the initial cluster centers:

(2) Repeat

(3) (Re) assign each object to the cluster to which the object is the most similar, based

on the mean value of the objects in the cluster;

(4) Update the cluster means, i.e., calculate the mean value of the objects for each

cluster;

(5) Until no change.

2.2.3 Searching

This module performs intensive searching of images from the database. User gives his

query image and based on various algorithms a set of images are generated. The query

is taken up and according to the algorithm given a set of related images is generated.

This module searches for the images in the database according to the specified

algorithm. To generate more relevant images, the number of search images can be

decreased. Now if the user is not satisfied by the images generated, he/she can

perform the search test again and again, until it generates the desired image.

Fig 2.1 Working of the SBIR System


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2.3 PERFORMANCE MATRICES

SBIR is essentially an information retrieval problem. Two of the most popular

evaluation measures are the,

PRECISION

The precision measures the proportion of the total images retrieved

which are relevant to the query. High precision means that less irrelevant images are returned or more relevant images are retrieved.

Precision = Number of relevant images retrieved

Total retrieved

RECALL

The recall measure is defined as the fraction of the all relevant images.

High recall indicates that few relevant images are missed

Recall = Total number of relevant images

Number of relevant images retrieved


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CHAPTER 3

REQUIREMENT ENGINEERING

The current technology and upgraded system devices offer a significant improvement

over the previous situation in labs where much effort was devoted to setting up the

software necessary for course activities. The hardware and softwares involved in the

project are briefly summarized below.

3.1 HARDWARE REQUIREMENTS

The hardware requirements may serve as the basis for a contract for the

implementation of the system and should therefore be a complete and consistent

specification of the whole system. They are used by software engineers as the starting

point for the system design. It should what the system do and not how it should be

implemented.

PROCESSOR: PENTIUM IV 2.6 GHz, Intel Core 2 Duo.

RAM: 512 MB DD RAM

MONITOR: 15 COLOR

HARD DISK: 40 GB

INTERNET: HIGH SPEED MODEM

KEYBOARD: STANDARD 102 KEYS

3.2 SOFTWARE REQUIREMENTS

The software requirements document is the specification of the system. It should

include both a definition and a specification of requirements. It is a set of what the

system should do rather than how it should do it. The software requirements provide a

basis for creating the software requirements specification. It is useful in estimating


18

cost, planning team activities, performing tasks and tracking the teams and tracking

the teams progress throughout the development activity.

OPERATING SYSTEM: Windows 7

LANGUAGE: JAVA / J2EE

FRAMEWORK: Java

DATABASE: mysql

TOOL USED: NetBeans IDE 7.0.1

3.3 FUNCTIONAL REQUIREMENTS

User can give his own choices. SBIR system allows users to select number of images

that has to be searched. The number of images can be altered by the user till he/she

doesnt get the correct image.

3.4 NON-FUNCTIONAL REQUIREMENTS

Secure access of confidential data (users details). SSL can be used. Also availability

has to be proper. Better component design to get better performance at peak time. And

flexible service based architecture will be highly desirable for future extension.


19

CHAPTER 4

ALGORITHMS DESCRIPITION

The main algorithms used to refrain the searching methodology are described below.

4.1 COLOR LAYOUT

Color is one of the most widely used features in image retrieval. It is robust to

background complication and invariant of image size and Orientation. As stated, three

major properties of color image similarity are usually considered

Area of Matching

Color Distance

Spatial Distribution

Area of matching is most commonly used because its idea is very clear and it can be

represented accurately by histograms. In most histogram representations, histogram

entries lay on the selected color space

4.2 COLOR SPACES

There are many color spaces designed for different systems and standards, but most of

them can be converted by a simple transformation. The color specifications will

display images on basis of their texture, background and many other criterias.

4.2.1 RGB (Red-Green-Blue)

Digital images are normally represented in RGB color space; it is the most commonly

use color space in computers. It is a device dependent color space, which used in CRT

monitors.


20

4.2.2 HSB (Hue Saturation Brightness)

It was used to model the properties of human perception. However it is inconvenient

to calculate color distance due to its discontinuity of hue at 360.

4.3 HISTOGRAMS

In color based image retrieval, histogram is the most commonly used representation

for color features. Statistically, it utilizes a property that images having similar

contents should have a similar color distribution.

However, histograms are not limited by describing the area of each color. The entries

of the histogram can also represent other features. For instant, texture features can be

represented by histograms formed by coefficients in transformed domain and the

value of each histogram entry represents the intensity of each coefficient.

4.3.1 Cumulative Color Histogram

As an alternative consideration that most color histograms are very sparse and

sensitive to noise and global color shifting, proposed using the cumulative color

histogram. It solved shortcomings of simple histogram with histogram intersection

since cumulative histogram does not have shifting problem.

4.3.2 Color Sets

To facilitate fast searching over large-scale image collections and improving the

compactness of the histogram, Smith and Chang proposed color sets as an

approximation to the color histograms. They first transformed the RGB color space

into a perceptually uniform space, such as CIE, and then quantized the transformed

color space into M bins. A color set is defined as a selection of colors from the

quantized color space.


21

4.3.3 Color Descriptor

Color is the most distinguishing visual features in image and video retrieval. It is

robust to changes in the background colors and is independent of image size and

orientation. Many forms of color distributions and representations are adopted in

JPEG, including some color-spatial descriptors such as Color Layout, Color Structure,

and some color quantization based descriptors such as Scalable Color and Dominant

Color.

4.3.4 Edge Histograms

The EHD represents the spatial distribution of edges in an image. The extraction

process of the EHD consists of the following stages:

The image array is divided into 4x4 sub images.

Each sub image is further partitioned into non-overlapping square image

blocks whose size depends on the resolution of the input image.

The edges in each image-block are categorized into one of the following six

types: vertical, horizontal, 45 diagonal, 135 diagonal, non-directional edge

and no-edge.

Now a 5-bin edge histogram of each sub image can be obtained.

Each bin value is normalized by the total number of image-blocks in the sub

image.

The normalized bin values are nonlinearly quantized. There are five types of

edge histogram:

Vertical edge

Horizontal edge

45 degree edge

135 degree edge

Non-directional edge


22

Fig 4.1 Five Types of Edge Histogram

Fig a shows vertical edge of histogram, fig b shows the horizontal edge of histogram,

fig c shows the degree edge of the histogram, fig d shows the 135 degree edge of

histogram and fig e shows the non-directional edge of histogram.

4.3.5 Dominant Color Descriptor (DCD)

DCD is a compact color descriptor, designed for small storage and high speed

retrieval. Image feature is formed by a small number of representative colors. These

colors are normally obtained by using clustering and color quantization. The

descriptor consists of the representative colors, their percentages in a region, spatial

coherency of the color, and color variance.

4.3.6 HSV Color Model

HSV color model stands for Hue Saturation Value colour model. This model

describes colors in terms of their shades and brightness (Luminance). This model

offers a more intuitive representation of relationship between colours.


23

4.3.7 Color Co-occurrence Matrix (CCM)

A co-occurrence matrix is a matrix that is defined over an image to be the distribution

of co-occurring values at a given offset. Value of an image is originally the grey scale

value of a specified pixel. The co-occurrence matrix is mainly used to measure the

texture of the image and hence it is used for texture analysis. The CCM for each pixel

of an image is found using the Hue Saturation Value (HSV) of the pixel and then

compared with CCM of the images in the database and the images are retrieved

Fig 4.2 SBIR using HSV Color Model


24

CHAPTER 5

TESTING

5.1 INTRODUCTION

Implementation is one of the most important tasks in project is the phase in which one

has to be cautions because all the efforts undertaken during the project will be very

interactive. Implementation is the most crucial stage in achieving successful system

and giving the users confidence that the new system is workable and effective. Each

program is tested individually at the time of development using the sample data and

has verified that these programs link together in the way specified in the program

specification. The computer system and its environment are tested to the satisfaction

of the user.

5.2 IMPLEMENTATION

The implementation phase is less creative than system design. It is primarily

concerned with user training, and file conversion. The system may be requiring

extensive user training. The initial parameters of the system should be modifies as a

result of a programming. A simple operating procedure is provided so that the user

can understand the different functions clearly and quickly. The different reports can

be obtained either on the inkjet or dot matrix printer, which is available at the disposal

of the user.

The proposed system is very easy to implement. In general implementation is used to

mean the process of converting a new or revised system design into an operational

one.


25

5.3 TESTING

Testing is the process where the test data is prepared and is used for testing the

modules individually and later the validation given for the fields.

Then the system testing takes place which makes sure that all components of the

system property functions as a unit.

The test data should be chosen such that it passed through all possible condition.

Actually testing is the state of implementation which aimed at ensuring that the

system works accurately and efficiently before the actual operation commence. The

following is the description of the testing strategies, which were carried out during the

testing period.

5.3.1 System Testing

Testing has become an integral part of any system or project especially in the field of

information technology. The importance of testing is a method of justifying, if one is

ready to move further, be it to be check if one is capable to with stand the rigors of a

particular situation cannot be underplayed and that is why testing before development

is so critical.

When the software is developed before it is given to user to user the software must be

tested whether it is solving the purpose for which it is developed. This testing

involves various types through which one can ensure the software is reliable.

The program was tested logically and pattern of execution of the program for a set of

data are repeated. Thus the code was exhaustively checked for all possible correct

data and the outcomes were also checked.

5.3.2 Module Testing

To locate errors, each module is tested individually. This enables us to detect error

and correct it without affecting any other modules. Whenever the program is not

satisfying the required function, it must be corrected to get the required result.


26

Thus all the modules are individually tested from bottom up starting with the smallest

and lowest modules and proceeding to the next level. Each module in the system is

tested separately. For example the job classification module is tested separately. This

module is tested with different job and its approximate execution time and the result

of the test is compared with the results that are prepared manually. The comparison

shows that the results proposed system works efficiently than the existing system.

Each module in the system is tested separately. In this system the resource

classification and job scheduling modules are tested separately and their

corresponding results are obtained which reduces the process waiting time.

5.3.3 Integration Testing

After the module testing, the integration testing is applied. When linking the modules

there may be chance for errors to occur, these errors are corrected by using this

testing.

In this system all modules are connected and tested. The testing results are very

correct. Thus the mapping of jobs with resources is done correctly by the system.

5.3.4 Acceptance Testing

When that user fined no major problems with its accuracy, the system passers through

a final acceptance test. This test confirms that the system needs the original goals,

objectives and requirements established during analysis without actual execution

which elimination wastage of time and money acceptance tests on the shoulders of

users and management, it is finally acceptable and ready for the operation.


27

CHAPTER 6

SYSTEM DESIGN

Design Engineering deals with the various UML [Unified Modeling language]

diagrams for the implementation of project. Design is a meaningful engineering

representation of a thing that is to be built. Software design is a process through which

the requirements are translated into representation of the software. Design is the place

where quality is rendered in software engineering. Design is the means to accurately

translate customer requirements into finished product.

6.1 ACTIVITY DIAGRAM

Activity diagram are a loosely defined diagram to show workflows of stepwise

activities and actions, with support for choice, iteration and concurrency. UML,

activity diagrams can be used to describe the business and operational step-by-step

workflows of components in a system. UML activity diagrams could potentially

model the internal logic of a complex operation. In many ways UML activity

diagrams are the object-oriented equivalent of flow charts and data flow diagrams

(DFDs) from structural development. The workflow and the flow of operations can be

clearly seen in the Fig 5.1. It represents the various operations undergoing during the

generation of Image.


28

Fig 6.1 Activity Diagram


29

6.2 USE CASE DIAGRAM

A Use case diagram is a list of steps, typically defining interactions between a role

(known in UML as an "actor") and a system, to achieve a goal. The actor can be a

human or an external system. A diagram is a type of behavioral diagram created from

a Use-case analysis. The purpose of use case is to present overview of the

functionality provided by the system in terms of actors, their goals and any

dependencies between those use cases.

Fig 6.2 Use Case Diagram of Admin


30

Fig 6.3 Use Case Diagram of User

6.3 DATA FLOW DIAGRAM

A data flow diagram (DFD) is a graphical representation of the "flow" of data

through an information system modeling its process aspects. Often they are a

preliminary step used to create an overview of the system which can later be

elaborated. DFDs can also be used for the visualization of data processing (structured

design)


31

Fig 6.4 Data Flow Diagram

6.4 SEQUENCE DIAGRAM

A sequence diagram shows object interactions arranged in time sequence. It depicts

the objects and classes involved in the scenario and the sequence of messages

exchanged between the objects needed to carry out the functionality of the scenario.

The sequence diagram shows a numerous steps which are involved in a procedure. It

also shows the exact functioning of each module and steps related to it. Sequence

diagram describes the whole functionality of the work that is carried out. Sequence

diagram shows interactions among the various modules of the work that has to be

carried out.


32

Fig 6.5 Sequence Diagram

The sequence diagram shows a numerous steps which are involved in a procedure. It

also shows the exact functioning of each module and steps related to it. Sequence

diagram describes the whole functionality of the work that is carried out. It depicts the

objects and classes involved in the scenario and the sequence of messages exchanged

between the objects needed to carry out the functionality of the scenario.


33

CHAPTER 7

GLOBAL STRUCTURE AND SUBSYSTEM

7.1 GLOBAL STRUCTURE OF A SYSTEM

The system was designed for databases containing relatively simple images, but even

in such cases large differences can occur among images in file size or resolution. In

addition, some images may be noisier, the extent and direction of illumination may

vary (fig 6.1) and so the feature vectors cannot be effectively compared. In order to

avoid it, a multistep pre-processing mechanism precedes the generation of descriptors.

In order to avoid it, a multistep pre-processing mechanism precedes the generation of

descriptors.

Fig 7.1 Same Image of different contrast


34

Fig 7.2 Global Structure of a System

Fig 7.3 System for Database containing simple images


35

CHAPTER 8

DEVELOPMENT TOOLS

8.1. GENERAL

This chapter is about the software language and the tools used in the development of

the project. The platform used here is JAVA. The Primary languages are JAVA and

J2EE. In this project JAVA is chosen for implementation.

8.2. FEATURES OF JAVA

8.2.1. THE JAVA FRAMEWORK

Java is a programming language originally developed by James Gosling at

Microsystems and released in 1995 as a core component of Sun Microsystems' Java

platform. The language derives much of its syntax from C and C++ but has a simpler

object model and fewer low-level facilities. Java applications are typically compiled

to byte code that can run on any Java Virtual Machine (JVM) regardless of computer

architecture. Java is general-purpose, concurrent, class-based, and object-oriented,

and is specifically designed to have as few implementation dependencies as possible.

It is intended to let application developers "write once, run anywhere".

Java is considered by many as one of the most influential programming languages of

the 20th century, and is widely used from application software to web applications the

java framework is a new platform independent that simplifies application

development internet. Java technology's versatility, efficiency, platform portability,

and security make it the ideal technology for network computing. From laptops to

datacenters, game consoles to scientific supercomputers, cell phones to the Internet,

Java is everywhere.


36

8.2.2. OBJECTIVES OF JAVA

Why Software Developers Choose Java?

Java has been tested, refined, extended, and proven by a dedicated community. And

numbering more than 6.5 million developers, it's the largest and most active on the

planet. With its versatility, efficiency, and portability, Java has become invaluable to

developers by enabling them to:

Write software on one platform and run it on virtually any other platform

Create programs to run within a Web browser and Web services

Develop server-side applications for online forums, stores, polls, HTML forms

processing, and more

Combine applications or services using the Java language to create highly

customized applications or services

Write powerful and efficient applications for mobile phones, remote

processors, low-cost consumer products, and practically any other device with

a digital heartbeat

Some Ways Software Developers Learn Java

Today, many colleges and universities offer courses in programming for the Java

platform. In addition, developers can also enhance their Java programming skills by

reading Sun's java.sun.com Web site, subscribing to Java technology-focused

newsletters, using the Java Tutorial and the New to Java Programming Center, and

signing up for Web, virtual, or instructor-led courses.

Object Oriented: To be an Object Oriented language, any language must follow at

least the four characteristics.

Inheritance: It is the process of creating the new classes and using the

behavior of the existing classes by extending them just to reuse the existing

code and adding addition a features as needed.

Encapsulation: It is the mechanism of combining the informationand

providing the abstraction.


37

Polymorphism: As the name suggest one name multiple form, Polymorphism

is the way of providing the different functionality by the functions having the

same name based on the signatures of the methods.

Dynamic binding: Sometimes we don't have the knowledge of objects about

their specific types while writing our code. It is the way of providing the

maximum functionality to a program about the specific type at runtime.


38

CHAPTER 9

IMPLEMENTATION

9.1. GENERAL

This chapter describes the implementation of searched based application. It deals with

the source code for main viewpoint of various services offered by a SBIR System.

9.2 IMPLEMENTAION OF SBIR SYSTEM

9.2.1 IMAGE COMPARISON

package cbir1;

import java.sql.*;

import java.util.StringTokenizer;

import javax.swing.ImageIcon;

import java.math.*;

import java.util.Vector;

public class compare {

DBConnection obj=new DBConnection();

ImageIcon ii;

Connection con;

Statement st;

int red[], green[], blue[], hue[], saturate[], value[];

double gfd[] = new double[80];

int count, flag = 0, val = 0;

String colorpath[];

double colorvalue[];


39

String shapepath[];

double shapevalue[];

String textpath[];

double textvalue[];

Vector cpath = new Vector();

Vector cvalue = new Vector();

Vector spath = new Vector();

Vector svalue = new Vector();

Vector tpath = new Vector();

Vector tvalue = new Vector();

double dr1[];

Vector v=new Vector();

public compare(ImageIcon ii) {

this.ii = ii;

}

public void colorcompare(ImageIcon ii) throws Exception {

int rsq, gsq, bsq, hsq, ssq, vsq;

red = new int[256];

green = new int[256];

blue = new int[256];

hue = new int[361];

saturate = new int[101];

value = new int[256];

color c = new color(ii);

c.histogram();

ResultSet rss = obj.countColor();

if (rss.next()) {

count = rss.getInt(1);

}

rss.close();

colorpath = new String[count];

colorvalue = new double[count];


40

int j = 0;

ResultSet rs1 = obj.selectColor();

while (rs1.next()) {

int r = 0, g = 0, b = 0, hu = 0, satu = 0, vl = 0;

colorpath[j] = rs1.getString(1);

int i = 0;

StringTokenizer sf = new StringTokenizer(rs1.getString(2), ",");

while (sf.hasMoreTokens()) {

red[i] = Integer.parseInt(sf.nextToken());

i++;

}

i = 0;

StringTokenizer sf1 = new StringTokenizer(rs1.getString(3), ",");

while (sf1.hasMoreTokens()) {

green[i] = Integer.parseInt(sf1.nextToken());

i++;

}

i = 0;



blue[i] = Integer.parseInt(sf2.nextToken());

i++;

}

i = 0;



hue[i] = Integer.parseInt(sf3.nextToken());

i++;

}

i = 0;




41

saturate[i] = Integer.parseInt(sf4.nextToken());

i++;

}

i = 0;



value[i] = Integer.parseInt(sf5.nextToken());

i++;

}

for (int k = 0; k < 256; k++) {

r += Math.pow((red[k] - c.red[k]), 2);

g += Math.pow(green[k] - c.green[k], 2);

b += Math.pow((blue[k] - c.blue[k]), 2);

vl += Math.pow(value[k] - c.value[k], 2);

}

for (int k = 0; k < 361; k++) {

hu += Math.pow((hue[k] - c.hue[k]), k);

}

for (int k = 0; k < 101; k++) {

satu += Math.pow((saturate[k] - c.saturate[k]), 2);

}

rsq = (int) Math.sqrt(r);

gsq = (int) Math.sqrt(g);

bsq = (int) Math.sqrt(b);

hsq = (int) Math.sqrt(hu);

ssq = (int) Math.sqrt(satu);

vsq = (int) Math.sqrt(vl);

colorvalue[j] = 100 - ((rsq + gsq + bsq + hsq + ssq + vsq) * 100 / 140000);

System.out.println(colorpath[j] + " " + colorvalue[j]);

if (colorvalue[j] > 40) {

cpath.addElement(colorpath[j]);

cvalue.addElement(colorvalue[j]);


42

}

j++;

}

for (int l = 0; l < cvalue.size(); l++) {

for (int u = l + 1; u < cvalue.size(); u++) {

if ((Double.parseDouble(cvalue.elementAt(l).toString())) <

(Double.parseDouble(cvalue.elementAt(u).toString()))) {

Object tval = cvalue.elementAt(l);

cvalue.setElementAt(cvalue.elementAt(u), l);

cvalue.setElementAt(tval, u);

Object tname = cpath.elementAt(l);

cpath.setElementAt(cpath.elementAt(u), l);

cpath.setElementAt(tname, u);

}

}

}

}

public void texturecompare(ImageIcon ii) throws Exception {

int ctsq, dsq, hsq, epsq, ensq, cnsq;

int j = 0;

dr1 = new double[count];

texture t = new texture(ii);

t.greyscale();

ResultSet rss = obj.countTexture();

if (rss.next()) {


}

rss.close();

textpath = new String[count];

textvalue = new double[count];

double textureValue = 0;

ResultSet rs1 = obj.selectTexture();


43


double ct = 0, dr = 0, hy = 0, hu = 0, ep = 0, en = 0, cn = 0;

textpath[j] = rs1.getString(1);

ct = Math.abs((Double.parseDouble(rs1.getString(2)) - t.contrast));

dr = Math.abs((Double.parseDouble(rs1.getString(3)) - t.dissimilar));

hy = Math.abs((Double.parseDouble( rs1.getString(4)) - t.homo));

ep = Math.abs((t.entropy - Double.parseDouble(rs1.getString(5))));

en = Math.abs((t.energy - Double.parseDouble(rs1.getString(6))));

cn = Math.abs((t.correlation - Double.parseDouble(rs1.getString(7))));

textvalue[j] += 100 - (ct / 250000);

textvalue[j] += 100 - (dr / 6000);

textvalue[j] += 100 - (hy);

textvalue[j] += 100 - (ep *10);

textvalue[j] += 100 - (en*10);

textvalue[j] += 100-(cn *10000 /5);

textvalue[j] /= 6;

System.out.println("text value[" + j + "]" + textvalue[j]);

if(textvalue[j]>50)

{

tpath.addElement(textpath[j]);

tvalue.addElement(textvalue[j]);

}

j++;

}

for (int l = 0; l < tvalue.size(); l++) {

for (int u = l + 1; u < tvalue.size(); u++) {

if ((Double.parseDouble(tvalue.elementAt(l).toString())) <

(Double.parseDouble(tvalue.elementAt(u).toString()))) {

Object t1value = tvalue.elementAt(l);

tvalue.setElementAt(tvalue.elementAt(u), l);

tvalue.setElementAt(t1value, u);

Object tname = tpath.elementAt(l);

tpath.setElementAt(tpath.elementAt(u), l);


44

tpath.setElementAt(tname, u);

}

}

}

}

public void shapecompare(ImageIcon ii) throws Exception {

int gfdsq;

shape p = new shape(ii);

p.gfd();

ResultSet rss = obj.countShape();

if (rss.next()) {


}

rss.close();

shapepath = new String[count];

shapevalue = new double[count];

int j = 0;

ResultSet rs2 = obj.selectShape();


int gf = 0;

shapepath[j] = rs2.getString(1);

int i = 0;

StringTokenizer sf = new StringTokenizer(rs2.getString(2), ",");

while (sf.hasMoreTokens()) {

gfd[i] = Double.parseDouble(sf.nextToken());

i++;

}

for (int k = 0; k < 80; k++) {

gf += Math.pow(gfd[k] - p.fd[k], 2);

}

gfdsq = (int) Math.sqrt(gf);

shapevalue[j] = 100 - (gfdsq / 178);

System.out.println("Shape value["+j+"]"+shapevalue[j]);


45

if (shapevalue[j] > 50) {

spath.addElement(shapepath[j]);

svalue.addElement(shapevalue[j]);

}

j++;

}

for (int l = 0; l < svalue.size(); l++) {

for (int u = l + 1; u < svalue.size(); u++) {

if ((Double.parseDouble(svalue.elementAt(l).toString())) <

(Double.parseDouble(svalue.elementAt(u).toString()))) {

Object shvalue = svalue.elementAt(l);

svalue.setElementAt(svalue.elementAt(u), l);

svalue.setElementAt(shvalue, u);

Object shname = spath.elementAt(l);

spath.setElementAt(spath.elementAt(u), l);

spath.setElementAt(shname, u);

}

}

}

for (int l = 0; l < svalue.size(); l++) {

System.out.println(spath.elementAt(l) + " ....... " + svalue.elementAt(l));

}

rs2.close();

}

}


46

CHAPTER 10

SNAPSHOTS

Snapshot is nothing but every moment of the application while running. It gives the

clear and elaborated application. It will be useful for the new user to understand for

the future steps

10.1 VARIOUS SNAPSHOTS

Fig 10.1 LOGIN FRAME


47

Fig 10.2 ADMIN HOME

Fig 10.3 ADD IMAGE


48

Fig 10.4 VIEW IMAGE

Figs 10.5 DELETE IMAGE


49

Fig 10.6 USER HOME_1

Fig 10.7 USER HOME_2


50

Fig 10.8 PAINT WITH OFF-SCREEN CANVAS


51

CHAPTER 11

CONCLUSION

This Project has enabled us to visualize a system that proves to be more accurate in

Searching and Extracting Images from databases. Unlike the conventional search

methods this technique has enabled us to achieve accuracy in data mining techniques

and a feedback system that enables users to get a panoramic view of how and why the

specific results were achieved thus making this system more reliable with transparent

operations. This project's implementation in the real time Content based image

retrieval systems is also of great significance since a fast accurate technique for

systems like Hospital records searches, law enforcement agencies databases and

government agencies record will not only improve the standards of service but also

make it easier for operators to continue their work.


52

CHAPTER 12

FUTURE ENHANCEMENTS

SBIR allow the populace to choose their representatives and express their preferences for

how they will be governed. Naturally, the integrity of this process is fundamental to the

integrity of Selectivity itself. The SBIR system must be sufficiently robust to withstand a

variety of fraudulent behaviors and must be sufficiently transparent and comprehensible

that candidates can accept the results.

The concept put forth in the project would remove the security threats from the present

SBIR system. A secured environment would allow users to find the exact image that they

are searching for.

For future work, we will extend the security parameters. New and improved pseudo

random algorithms will be applied for the safety of the platform. Also the randomness of

the generation of the secret key would be made more complex. One method to do that is

to increase the number of the rounds of the algorithm.


53

CHAPTER 13

REFERENCES

[1] D. Comaniciu, and P. Meer, Robust analysis of feature spaces: color image

segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp. 750

755, June 1997.

[2] N. Dalal, and B. Triggs, Histograms of oriented gradients for human detection,

IEEE Conference on Computer Vision and Pattern Recognition, pp. 886893, July 2005.

[3] T. Deselaers, D. Keysers, and H. Ney, Features for image retrieval: an experimental

comparison, Information Retrieval, vol. 11, pp. 77107, December 2007.

[4] M. Eitz, K. Hildebrand, T. Boubekeur, and M. Alexa, An evaluation of descriptors

for large-scale image retrieval from sketched feature lines, Computers and Graphics,

vol. 34, pp. 482498, October 2010.

[5] R. Fabbri, L.D.F. Costa, J.C. Torelli, and O.M. Bruno, 2D Euclidean distance

transform algorithms: a comparative survey, ACM Computing Surveys, vol. 44, pp. 1

44, February 2008.

[6] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Hiang, B. Dom, M. Gorkani, J.

Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, Query by image and video

content: the QBIC system, IEEE Computer, vol. 28, pp. 2332, 2002.

[7] Gy. Gyorok,Embedded hybrid controller with programmable analog circuit, IEEE

14th International Conference on Intelligent Systems pp. 59.159.4, May 2010.

[8] R. Hu, M. Barnard, and J. Collomosse, Gradient _eld descriptor for sketch based

image retrieval and localization, International Conference on Image Processing, pp. 1

4, 2010.

[9] A.K. Jain, J.E. Lee, and R. Jin, Sketch to photo matching: a feature-based approach,

Proc. SPIE, Biometric Technology for Human Identi_cation VII, vol. 7667, pp. 766702

766702,2010.


54

[10] A.K. Jain, J.E. Lee, R. Jin, and N. Gregg, Graff_ti-ID: matching retrieval of graffti

images, ACM MM, MiFor09, pp. 16, 2009.

[11] J.B. Kruskal, Nonmetric multidimensional scaling: a numerical method,

Psychometrika, vol. 29, pp. 115129, 1964.

[12] Y. Liu, and F. Dellaert, A classi_cation based similarity metric for 3D image

retrieval, IEEE Conference on Computer Vision and Pattern Recognition, pp. 800805,

June 1998.

[13] D.G. Lowe, Distinctive image features from scale-invariant keypoints,

International Journal of Computer Vision, vol. 60, pp. 91110, 2004.

[14] D.G. Lowe, Object Recognition from Local Scale-Invariant Features, IEEE

International Conference on Computer Vision, vol. 2, p. 1150, 1999.

[15] J.R. Smith, and S.F. Chang, VisualSEEK: a fully automated contentbased image

query system, ACM Multimedia 96, pp. 9798, 1996.

[16] J. Tick, and J. Fodor, Some classes of binary operations in approximate reasoning,

Studies in Informatics and Control, vol. 15, pp. 259270, 2006.

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