CONTENT BASED IMAGE RETRIEVAL SYSTEM

CONTENT BASED IMAGE RETRIEVAL SYSTEM
1. INTRODUCTION
1.1 MOTIVATION:
In many areas of commerce, government, academia, and hospitals, large
collections of digital images are being created. Many of these collections are the
product of digitizing existing collections of analogue photographs, diagrams,
drawings, paintings, and prints. Usually, the only way of searching these collections
was by keyword indexing, or simply by browsing. Digital images databases however,
open the way to content-based searching.
1.2 PROBLEM DEFINITION:
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 providing 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
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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 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 observer’s 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.
1.3 OBJECTIVE OF PROJECT
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 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
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of features (such as color,Texture and shape) that can be automatically extracted from
the images themselves. The features used for 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.
• Feature extraction – Analyze raw image data to extract feature specific
information.
• Feature storage – Provide efficient storage for the extracted information, also
help to improve searching speed.
• Similarity measure – Measure the difference between images for determining
the relevance between images.
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2. LITERATURE SURVEY
Literature survey is the most important step in software development process.
Before developing the tool it is necessary to determine the time factor, economy n
company strength. Once these things r satisfied, ten next step is to determine which
operating system and language can be used for developing the tool. Once the
programmers start building the tool the programmers need lot of external support.
This support can be obtained from senior programmers, from book or from websites.
Before building the system the above consideration r taken into account for
developing the proposed system.
2.1 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
2.1.1 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
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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 (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.
2.1.2 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.
2.1.3 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 precompile the data structure. Using these techniques, HISA is able to bridge the
gap between the image semantics and the visual features, therefore providing more
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user-friendly and high performance queries. We demonstrate the novel data structure
employed by HISA, the query algorithms, and the pre-computation process.
2.1.4 Ontology-Based Query Expansion Widget for information Retrieval
In this project we present an ontology-based query expansion widget which
utilizes the ontologies 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, domainspecic and spatiotemporal ontologies.
2.1.5 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 [9]. 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.
2.2 DISADVANTAGES:
• The performances of these systems are not satisfactory.
• Time taking process.
• Less iterative process.
• No efficiency.
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2.3 PROPOSED SYSTEM:
Relevance feedback is an interactive process that starts with normal CBIR.
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 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.
Digital images databases open the way for content-based searching. Content
Based Image Retrieval occupies a well ranked position among the research areas as it
provides the practical solution for narrowing the semantic gap between the image
retrieval process and the human perception. The main objective of this paper is to
propose a framework for region content based image retrieval based on a distributed
clustered image dataset. The proposed framework introduces a new perspective to
measure the similarity between the image query and the clustered dataset images.
Moreover, a development by adopting three relevance feedback techniques is used to
refine the results of the retrieval system which are the well known Query
Point Movement and Query Expansion, besides to the proposed third
technique which is Query Modified Re-Weighting technique.
MODULES:
• INDEXING:
In Indexing module we are going to giving the numbering to all the
images. According to the numbers this application is going to retrieving the images.
• SEARCHING:
In this module we are going search for the images whatever we selected
for the search.
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Figure1: PROCESS DIAGRAM
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Input
Query
Retrieval
Result
Feature
Extractio
n
Similarit
y
measure
Query
update
User’s
Feedbac
k
Find
all
image
s?
Final
Retrieval
Result

3. ANALYSIS
3.1 PROBLEM STATEMENT:
The problem involves entering an image as a query in 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 which are visually
similar to query image.
3.2 APPROACH METHOD:
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.
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.
3.3 SOFTWARE REQUIREMENTS SPECIFICATION:
Introduction:
Software Requirements Specification plays an important role in creating
quality software solutions. Specification is basically a representation process.
Requirements are represented in a manner that ultimately leads to successful software
implementation.The software requirements specification is produced at the
culmination of the analysis task. The function and performance allocated to the
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software as a part of system engineering are refined by establishing a complete
information description, a detailed functional and behavioral description, and
indication of performance requirements and design constraints, appropriate validation
criteria and other data pertinent to requirements.
3.3.1 SOFTWARE DEVELOPMENT LIFE CYCLE:
The term software development life cycle model is a way of describing the
planning, designing, coding, and testing of a software system, as well as the method in
which these steps are implemented. A variety of life cycle models exist, but they all
include the same constituent parts. All life cycle models take a project through several
primary phases: a requirements-gathering phase, a design phase, a construction or
implementation phase, and a testing phase. Each phase produces feedback that affects
the next phase. For instance, the requirements gathered during the requirements phase
influence the design, which is translated into working software code during the
implementation phase. The software code is verified against the requirements during
the testing phase.
PROTOTYPE MODEL:
The prototyping paradigm begins with requirements adhering. Developer and
customer meet and dine the overall objectives for the software, identify whatever
requirements are known, and outline areas where further definition is mandatory. A
"quick design" then occurs. The quick design focuses on a representation of those
aspects of the software that will be visible to the customer/user . The quick design
leads to the construction of a prototype. The prototype is evaluated by the
customer/user and used to refine requirements for the software to be developed.
Iteration occurs as the prototype is tuned to satisfy the needs of the customer, while at
the same time enabling the developer to better understand what needs to be done.
Ideally, the prototype serves as a mechanism for identifying software requirements. If
a working prototype is built, the developer attempts to use existing program fragments
or applies tools that enable working programs to be generated quickly.
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RAD MODEL:
Rapid application development (RAD) is an incremental software
development process model that emphasizes an extremely short development cycle.
The RAD model is a “high-speed” adaptation of the linear sequential model in which
rapid development is achieved by using component-based construction. If
requirements are well understood and project scope is constrained, the RAD process
enables a development team to create a “fully functional system” within very short
time periods (e.g., 60 to 90 days) .
INCREMENTAL MODEL:
The incremental model combines elements of the linear sequential model with
the iterative philosophy of prototyping. Each linear sequence produces a deliverable
“increment” of the software.When an incremental model is used, the first increment is
often a core product. That is, basic requirements are addressed, but many
supplementary features (some known, others unknown) remain undelivered. The core
product is used by the customer (or undergoes detailed review). As a result of use
and/or evaluation, a plan is developed for the next increment. The plan addresses the
modiﬁcation of the core product to better meet the needs of the customer and the
delivery of additional features and functionality. This process is repeated following
the delivery of each increment, until the complete product is produced.
SPIRAL MODEL:
The spiral model, originally proposed by Boehm , is an evolutionary software
process model that couples the iterative nature of prototyping with the controlled and
systematic aspects of the linear sequential model. It provides the potential for rapid
development of incremental versions of the software. Using the spiral model, software
is developed in a series of incremental releases. During early iterations, the
incremental release might be a paper model or prototype. During later
iterations,increasingly more complete versions of the engineered system are produced.
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CONCURRENT DEVELOPMENT MODEL:
The concurrent process model can be represented schematically as a series of
major technical activities, tasks, and their associated states. All activities exist
concurrently but reside in different states. The concurrent process model deﬁnes a
series of events that will trigger transitions from state to state for each of the software
engineering activities. This generates the event analysis model correction which will
trigger the analysis activity from the done state into the awaiting changes state. In
reality, the concurrent process model is applicable to all types of software
development and provides an accurate picture of the current state of a project. Rather
than, confining software engineering activities to a sequence of events, it defines a
network of activities. Each activity on the network exists simultaneously with other
activities. Events generated within a given activity or at some other place in the
activity network trigger transitions among the states of an activity
WATER FALL MODEL:
The model that is basically being followed is the WATER FALL MODEL,
which states that the phases are organized in a linear order. First of all the feasibility
study is done. Once that part is over the requirement analysis and project planning
begins. If system exists one and modification and addition of new module is needed,
analysis of present system can be used as basic model.
The design starts after the requirement analysis is complete and the coding
begins after the design is complete. Once the programming is completed, the testing is
done. In this model the sequence of activities performed in a software development
project are: -
Requirement Analysis, Project Planning, System design, Detail design,
Coding, Unit testing, System integration & testing, here the linear ordering of these
activities is critical. End of the phase and the output of one phase is the input of other
phase. The output of each phase is to be consistent with the overall requirement of the
system. Some of the qualities of spiral model are also incorporated like after the
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people concerned with the project review completion of each of the phase the work
done.
WATER FALL MODEL was being chosen because all requirements were
known beforehand and the objective of our software development is the
computerization/automation of an already existing manual working system.
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Figure2: WATER FALL MODEL
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3.3.2 Software Requirements:
• Operating system :- Windows XP Professional
• Front End :- JAVA, Swing
• Tool :- Eclipse 3.3
3.3.3 Hardware Requirements:
• System : Pentium IV 2.4 GHz.
• Hard Disk : 40 GB.
• Floppy Drive : 1.44 Mb.
• Monitor : 15 VGA Colour.
• Mouse : Logitech.
• Ram : 256 Mb.
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4. DESIGN
INTRODUCTION:
Software design sits at the technical kernel of the software engineering process
and is applied regardless of the development paradigm and area of application. Design
is the first step in the development phase for any engineered product or system. The
designer’s goal is to produce a model or representation of an entity that will later be
built. Beginning, once system requirement have been specified and analyzed, system
design is the first of the three technical activities -design, code and test that is required
to build and verify software.
The importance can be stated with a single word “Quality”. Design is the place
where quality is fostered in software development. Design provides us with
representations of software that can assess for quality. Design is the only way that we
can accurately translate a customer’s view into a finished software product or system.
Software design serves as a foundation for all the software engineering steps that
follow. Without a strong design we risk building an unstable system – one that will be
difficult to test, one whose quality cannot be assessed until the last stage.
During design, progressive refinement of data structure, program structure,
and procedural details are developed reviewed and documented. System design can be
viewed from either technical or project management perspective. From the technical
point of view, design is comprised of four activities – architectural design, data
structure design, interface design and procedural design.
UNIFIED MODELING LANGUAGE:
Building a model for a software system prior to its construction is as
essential as having a blueprint for building a large building. Good models are
essential for communication among project teams. As the complexity of the
systems increases, so does the importance of good modeling techniques.
• The unified modeling language allows the software engineer to express an
analysis model using the modeling notation that is governed by a set of
syntactic semantic and pragmatic rules.
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• A UML system is represented using five different views that describe the
system from distinctly different perspective. Each view is defined by a set of
diagram, which is as follows.
4.2 ARCHITTECTURAL VIEWS AND DIAGRAMS OF THE
UML:
The UML Meta model elements are organized into diagrams. Different
diagrams are used for different purposes depending on the angle from which you
are viewing the system. The different views are called “architectural views”.
Architectural views facilitate the organization of knowledge, and diagrams enable the
communication of knowledge. Then knowledge itself is within the model or set of
models that focuses on the problem and solution. The architectural views and their
diagrams are summarized below:
• The “user model view” encompasses a problem and solution
from the preservative of those individuals whose problem the solution
addresses. The view presents the goals and objectives of the problem owners
and their requirements of the solution. This view is composed of “use case
diagrams”. These diagrams describe the functionality provided by a system to
external integrators. These diagrams contain actors, use cases, and their
relationships.
4.3 UML DIAGRAMS:
Every complex system is best approached through a small set of nearly
independent views of a model; no single viewer is sufficient. Every model may
be expressed at different levels of fidelity. The best models are connected to reality.
The UML defines nine graphical diagrams.
1. Class diagram
2. Object diagram
3. Use-case diagram
4. Sequence diagram
5. Collaboration diagram
6. Activity diagram
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7. State Chart diagram
8. Component diagram
9. Deployment diagram
CLASS DIAGRAM:
Class diagrams are the most common diagrams found in modeling object-
oriented systems. A class diagram shows a set of classes, interfaces, and
collaborations and their relationships. Graphically, a class diagram is a collection of
vertices and arcs.
USE CASE DIAGRAM:
Use Case diagrams are one of the five diagrams in the UML for modeling the
dynamic aspects of systems(activity diagrams, sequence diagrams, state chart
diagrams and collaboration diagrams are the four other kinds of diagrams in the UML
for modeling the dynamic aspects of systems). Use Case diagrams are central to
modeling the behavior of the system, a sub-system, or a class. Each one shows a set of
use cases and actors and relationships.
SEQUENCE DIAGRAM:
A sequence diagram is an interaction diagram that emphasizes the time
ordering of the messages. Graphically, a sequence diagram is a table that shows
objects arranged along the X-axis and messages, ordered in increasing time, along the
Y-axis.
COLABORATION DIAGRAM:
Collaboration diagrams render how behavior is realized by components
with in a system. These diagrams contain classes, associations, and their message
exchanges with in a collaboration to accomplish a purpose. In generic form,
these diagrams describe a set of classes and associations involved in message
exchange sequences. In instance form(scenarios), these diagrams describe a set of
objects of those classes links confirming to the associations, and one actual
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message exchange sequence that inconsistent with the generic form and uses
those objects and links
ACTIVITY DIAGRAM:
An Activity Diagram is essentially a flow chart showing flow of control from
activity to activity. They are used to model the dynamic aspects of as system. They
can also be used to model the flow of an object as it moves from state to state at
different points in the flow of control.
STATE CHART DIAGRAM:
A state chart diagram shows a state machine. State chart diagrams are used to
model the dynamic aspects of the system. For the most part this involves modeling the
behavior of the reactive objects. A reactive object is one whose behavior is best
characterized by its response to events dispatched from outside its context. A reactive
object has a clear lifeline whose current behavior is affected by its past. Graphically a
state chart diagram is a collection of vertices and arcs.
COMPONENT DIAGRAM:
A component diagram shows a set of components and their relationships. We
use the component diagram to illustrate the static implementation view of a system.
Component diagrams are related to class diagrams in that a component typically maps
to one or more classes, interfaces, or collaborations.
DEPLOYMENT DIAGRAM:
A deployment diagram shows a set of nodes and their relationships. We use
deployment diagrams to illustrate the static deployment view of architecture.
Deployment diagrams are related to component diagrams in that a node typically
encloses one or more components.
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4.1 UML DIAGRAMS
USE CASE DIAGRAM:
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CLASS DIAGRAM:
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SEQUENCE DIAGRAM:
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COLLABORATION DIAGRAM:
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STATE CHART DIAGRAM:
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COMPONENT DIAGRAM:
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DEPLOYMENT DIAGRAM:
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5. IMPLEMENTATION
SOFTWARE DESCRIPTION:
JAVA:
Java is a set of several computer software products and specifications
from Sun Microsystems (which has since merged with Oracle Corporation), that
together provide a system for developing application software and deploying it in
a cross-platform computing environment. Java is used in a wide variety of computing
platforms from embedded devices and mobile phones on the low end, to enterprise
servers and supercomputers on the high end. While less common, Java applets are
sometimes used to provide improved and secure functions while browsing the World
Wide Web on desktop computers.
Writing in the Java programming language is the primary way to produce code
that will be deployed as Java bytecode. There are, however,
bytecodecompilers available for other languages such as Ada, JavaScript, Python,
and Ruby. Several new languages have been designed to run natively on theJava
Virtual Machine (JVM), such as Scala, Clojure and Groovy. Java syntax borrows
heavily from C and C++, but object-oriented features are modeled
after Smalltalk and Objective-C.[6]
Java eliminates certain low-level constructs such
as pointers and has a very simple memory model where every object is allocated on
the heap and all variables of object types are references. Memory management is
handled through integrated automatic garbage collection performed by the JVM
An edition of the Java platform is the name for a bundle of related programs
from Sun that allow for developing and running programs written in the Java
programming language. The platform is not specific to any one processor or operating
system, but rather an execution engine (called a virtual machine) and a compiler with
a set of libraries that are implemented for various hardware and operating systems so
that Java programs can run identically on all of them.
• Java Card: A technology that allows small Java-based applications (applets) to
be run securely on smart cards and similar small-memory devices.
• Java ME (Micro Edition): Specifies several different sets of libraries (known
as profiles) for devices with limited storage, display, and power capacities.
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Often used to develop applications for mobile devices, PDAs, TV set-top
boxes, and printers.
• Java SE (Standard Edition): For general-purpose use on desktop PCs, servers
and similar devices.
• Java EE (Enterprise Edition): Java SE plus various APIs useful for multi-
tier client–server enterprise applications.
The Java platform consists of several programs, each of which provides a
portion of its overall capabilities. For example, the Java compiler, which converts
Java source code into Java byte code (an intermediate language for the JVM), is
provided as part of the Java Development Kit (JDK). The Java Runtime
Environment (JRE), complementing the JVM with a just-in-time (JIT) compiler,
converts intermediate byte code into native machine code on the fly. An extensive set
of libraries are also part of the Java platform.
The essential components in the platform are the Java language compiler, the
libraries, and the runtime environment in which Java intermediate byte code
"executes" according to the rules laid out in the virtual machine specification.
Java Virtual Machine:
The heart of the Java platform is the concept of a "virtual machine" that
executes Java byte code programs. This byte code is the same no matter what
hardware or operating system the program is running under. There is a JIT(Just In
Time) compiler within the Java Virtual Machine, or JVM. The JIT compiler translates
the Java bytecode into native processor instructions at run-time and caches the native
code in memory during execution.
The use of bytecode as an intermediate language permits Java programs to run
on any platform that has a virtual machine available. The use of a JIT compiler means
that Java applications, after a short delay during loading and once they have "warmed
up" by being all or mostly JIT-compiled, tend to run about as fast as native
programs. Since JRE version 1.2, Sun's JVM implementation has included a just-in-
time compiler instead of an interpreter.
Although Java programs are cross-platform or platform independent, the code
of the Java Virtual Machines (JVM) that execute these programs is not. Every
supported operating platform has its own JVM.
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Class libraries:
In most modern operating systems (OSs), a large body of reusable code is
provided to simplify the programmer's job. This code is typically provided as a set
of dynamically loadable libraries that applications can call at runtime. Because the
Java platform is not dependent on any specific operating system, applications cannot
rely on any of the pre-existing OS libraries. Instead, the Java platform provides a
comprehensive set of its own standard class libraries containing much of the same
reusable functions commonly found in modern operating systems. Most of the system
library is also written in Java.
For instance, Swing library paints the user interface and handles the events
itself, eliminating many subtle differences between how different platforms handle
even similar components.
The Java class libraries serve three purposes within the Java platform. First,
like other standard code libraries, the Java libraries provide the programmer a well-
known set of functions to perform common tasks, such as maintaining lists of items or
performing complex string parsing. Second, the class libraries provide an abstract
interface to tasks that would normally depend heavily on the hardware and operating
system. Tasks such as network access and file access are often heavily intertwined
with distinctive implementations of each platform. The java.net andjava.io libraries
implement an abstraction layer in native OS code, then provide a standard interface
for the Java applications to perform those tasks. Finally, when some underlying
platform does not support all of the features a Java application expects, the class
libraries work to gracefully handle the absent components, either by emulation to
provide a substitute, or at least by providing a consistent way to check for the
presence of a specific feature
Implementation is the stage of the project when the theoretical design is turned
out into a working system. Thus it can be considered to be the most critical stage in
achieving a successful new system and in giving the user, confidence that the new
system will work and be effective.
The implementation stage involves careful planning, investigation of the
existing system and it’s constraints on implementation, designing of methods to
achieve changeover and evaluation of changeover methods.
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6. OUTPUT SCREENS
Click on “open dir…” and select a folder
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First index a particular folder. So that retrieval will be fast.
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A folder is selected and it has been indexed
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The method of retrieval is selected. Here, edge histogram.
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Select a image to give as input
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After selecting the method of retrieval , searching is performed.
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An image have been opened to retrieve the resembling image.
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Resembling images have been retrieved.
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We can see number of files are indexed
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Color layout is selected for image retrieval.
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Select image to search for..
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Similar Images have been retrieved
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Image is retrieved by index number
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Select a dimension to search
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Select a image to create a result
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7. TESTING
INTRODUCTION:
Unit testing:
Unit testing involves the design of test cases that validate that the internal
program logic is functioning properly, and that program inputs produce valid outputs.
All decision branches and internal code flow should be validated. It is the testing of
individual software units of the application .it is done after the completion of an
individual unit before integration. This is a structural testing, that relies on knowledge
of its construction and is invasive. Unit tests perform basic tests at component level
and test a specific business process, application, and/or system configuration. Unit
tests ensure that each unique path of a business process performs accurately to the
documented specifications and contains clearly defined inputs and expected results.
Integration testing:
Integration tests are designed to test integrated software components to
determine if they actually run as one program. Testing is event driven and is more
concerned with the basic outcome of screens or fields. Integration tests demonstrate
that although the components were individually satisfaction, as shown by successfully
unit testing, the combination of components is correct and consistent. Integration
testing is specifically aimed at exposing the problems that arise from the
combination of components.
Functional testing:
Functional tests provide systematic demonstrations that functions tested are
available as specified by the business and technical requirements, system
documentation, and user manuals.
Functional testing is centered on the following items:
45

Valid Input : identified classes of valid input must be accepted.
Invalid Input : identified classes of invalid input must be rejected.
Functions : identified functions must be exercised.
Output : identified classes of application outputs must be exercised.
Systems/Procedures: interfacing systems or procedures must be invoked.
Organization and preparation of functional tests is focused on requirements,
key functions, or special test cases. In addition, systematic coverage pertaining to
identify Business process flows; data fields, predefined processes, and successive
processes must be considered for testing. Before functional testing is complete,
additional tests are identified and the effective value of current tests is determined.
System Testing:
System testing ensures that the entire integrated software system meets
requirements. It tests a configuration to ensure known and predictable results. An
example of system testing is the configuration oriented system integration test.
System testing is based on process descriptions and flows, emphasizing pre-driven
process links and integration points.
White Box Testing:
White Box Testing is a testing in which in which the software tester has
knowledge of the inner workings, structure and language of the software, or at least its
purpose. It is purpose. It is used to test areas that cannot be reached from a black box
level.
Black Box Testing:
Black Box Testing is testing the software without any knowledge of the inner
workings, structure or language of the module being tested. Black box tests, as most
other kinds of tests, must be written from a definitive source document, such as
specification or requirements document, such as specification or
46

requirements document. It is a testing in which the software under test is treated, as a
black box .you cannot “see” into it. The test provides inputs and responds to outputs
without considering how the software works.
7.1 DESIGN OF TEST CASES AND SCENARIOS
7.2 VALIDATION:
As per given Software Requirements and Hardware Requirements, project has
been fulfilled all the Requirements and works efficiently. we validated by considering
above Test Cases .
8. CONCLUSION
TEST CASE
NUMBER
DESCRIPTION EXPECTED
OUTPUT
OUTPUT RESULT
1 Indexing a folder
and searching other
folder
Invalid Retrieving Invalid Retrieving success
2 Searching without
Indexing
Invalid Retrieving Invalid Retrieving success
3 Indexing and
Searching same
folder
Similarity images
are retrieved
Similarity images
are retrieved
success
4 Browsing with
invalid Index
number
Invalid retrieving Invalid Retrieving success
47

Among the objectives of this paper performed to design, implement and test a
sketch-based image retrieval system. Two main aspects were taken into account. The
retrieval process has to be unconventional and highly interactive. The robustness of
the method is essential in some degree of noise, which might also be in case of simple
images. The drawn image without modification cannot be compared with color image,
or its edge representation. Alternatively a distance transform step was introduced. The
simple smoothing and edge detection based method was improved, which had a
similar importance as the previous step.
FUTURE SCOPE:
At the tests the effectiveness of EHD and the dynamically parameterized HOG
implementation was compared. It was examined with more databases. In our
experience the HOG in more cases was much better than the EHD based retrieval.
However, the situation is not so simple. The edge histogram descriptor can mainly
look better for information poor sketches, while in other case better results can be
achieved for more detailed. This is due to the sliding window solution of HOG.
REFERENCES
48

[1] Multimedia Content Description Interface – Part 3: Visual, ISO/IEC
JTC1/SC29/WG11/N4062, 2008.
[2] J. Wang and G. Wiederhold, “SIMPLIcity: Semantics-Sensitive Integrated
Matching for Picture Libraries,” IEEE Transactions On Pattern Analysis And
Machine Intelligence, vol. 23, no. 8, pp. 1-17, September 2008.
[3] M. Swain and D. Ballard, “Color indexing,” International Journal of Computer
Vision, vol.. 7, no.1, pp. 11-32, 2008.
[4] K. Hirata and T. Kato, "Query by visual example, content based image retrieval,"
Advances in Database Technology-EDBT'92, vol. 580, pp. 56-71, A. Pirotte,
C.Delobel, and G. Gottlob, Eds., 2006, Springer-Verlag.
[5] J. Jeon, V. Lavrenko, and R. Manmatha, “Automatic Image Annotation and
Retrieval Using Cross-Media Relevance Models,” Proc. 26th Ann. Int’l ACM SIGIR
Conf. Research and Development in Information Retrieval (SIGIR ’03), pp. 119-126,
2003.
[6] J. Kalervo, K. Jaana, and N. Timo, “ExpansionTool: Concept-Based
Query Expansion and Construction,” Information Retrieval, vol. 4, no. 3, pp. 231-
255, 2001.
[7] G. Chen, X. Li, L. Shou, J. Dong, and C. Chen, “HISA: A Query
System Bridging the Semantic Gap for Large Image Databases (Demo),” Proc. 32nd
Int’l Conf. Very Large Data Bases (VLDB ’06), pp. 1187-1190, 2006.
[8] X.Y. Li, L.D. Shou, G. Chen, and K.-L. Tan, “An Image-Semantic
Ontological Framework for Large Image Databases (Poster),” Proc. 12th Int’l Conf.
Database Systems for Advanced Applications (DASFAA ’07), pp. 1050-1053, 2007.
[9] J.R. Paek and S. Smith, “Detecting Image Purpose in World-Wide Web
Documents,” Proc. IS&T/SPIE Symp. Electronic Imaging: Science and Technology—
Document Recognition, vol. 3305, pp. 151- 158, Jan. 1998.
[10] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-
Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis
and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
APPENDIX
SAMPLE CODE:
49

public class MainFrame extends JFrame
{
private Color highlightHoverColor = Color.decode("#dddddd");
private Color highlightSelectColor = Color.decode("#eeeeee");
private SearchResultsTableModel tableModel = new SearchResultsTableModel();
private IndexReader browseReader = null;
public MainFrame() {
try {
UIManager.setLookAndFeel(UIManager.getSystemLookAndFeelClassName(
));
} catch (Exception e) {
}
initComponents();
try {
Image icon =
ImageIO.read(getClass().getResource("/resources/viewmag16.png"));
if (icon!=null) setIconImage(icon);
} catch (IOException ex) {
Logger.getLogger(MainFrame.class.getName()).log(Level.SEVERE, null, ex);
}
selectboxDocumentBuilder.setSelectedIndex(5);
buttonSwitchIndex.setBackground(highlightSelectColor);
DropTarget t = new DropTarget(searchPanel, new DropTargetListener() {
public void dragEnter(DropTargetDragEvent dtde) {
}
public void dragOver(DropTargetDragEvent dtde) {
50

}
public void dropActionChanged(DropTargetDragEvent dtde) {
}
public void dragExit(DropTargetEvent dte) {
}
public void drop(DropTargetDropEvent dtde) {
try {
Transferable tr = dtde.getTransferable();
DataFlavor[] flavors = tr.getTransferDataFlavors();
for (int i = 0; i < flavors.length; i++) {
System.out.println("Possible flavor: " + flavors[i].getMimeType());
if (flavors[i].isFlavorJavaFileListType()) {
dtde.acceptDrop(DnDConstants.ACTION_COPY_OR_MOVE);
java.util.List list = (java.util.List) tr.getTransferData(flavors[i]);
textfieldSearchImage.setText(list.get(0).toString());
dtde.dropComplete(true);
return;
}
}
} catch (Exception e) {
e.printStackTrace();
}
}
});
}
textfieldIndexDir.setEditable(false);
textfieldIndexDir.addActionListener(new java.awt.event.ActionListener() {
51

public void actionPerformed(java.awt.event.ActionEvent evt) {
textfieldIndexDirActionPerformed(evt);
}
});
buttonOpenDir.setText(bundle.getString("button.open.indexdirectory")); //
NOI18N
buttonOpenDir.addActionListener(new java.awt.event.ActionListener() {
buttonOpenDirActionPerformed(evt);
}
});
buttonStartIndexing.setText(bundle.getString("button.start.indexing")); //
NOI18N
buttonStartIndexing.addActionListener(new java.awt.event.ActionListener() {
buttonStartIndexingActionPerformed(evt);
}
});
progressBarIndexing.setFocusable(false);
progressBarIndexing.setName(bundle.getString("progressbar.indexing.name")); /
/ NOI18N
progressBarIndexing.setString(bundle.getString("progressbar.indexing.name"));
// NOI18N
progressBarIndexing.setStringPainted(true);
textfieldSearchImage.setEditable(false);
buttonOpenImage.setText(bundle.getString("button.open.searchimage")); //
NOI18N
buttonOpenImage.setActionCommand(bundle.getString("openImageButton")); //
NOI18N
buttonOpenImage.addActionListener(new java.awt.event.ActionListener() {
52

buttonOpenImageActionPerformed(evt);
}
});
buttonStartSearch.setText(bundle.getString("button.start.search")); // NOI18N
buttonStartSearch.addActionListener(new java.awt.event.ActionListener() {
buttonStartSearchActionPerformed(evt);
}
});
progressSearch.setString("Search state ...");
progressSearch.setStringPainted(true);
buttonStartMosaicing.setText(bundle.getString("button.start.search")); // NOI18N
buttonStartMosaicing.setToolTipText(bundle.getString("mosaic.startButton.toolt
ip")); // NOI18N
buttonStartMosaicing.addActionListener(new java.awt.event.ActionListener() {
buttonStartMosaicingActionPerformed(evt);
}
});
progressMosaic.setToolTipText(bundle.getString("mosaic.progress.tooltip")); //
NOI18N
progressMosaic.setString(bundle.getString("progressbar.mosaic.name")); //
NOI18N
progressMosaic.setStringPainted(true);
labelMosaicTitle.setFont(new java.awt.Font("Tahoma", 1, 18));
labelMosaicTitle.setText("Select image to create Mosaic:");
mosaicTileCountSlider.setPaintLabels(true);
53

mosaicTileCountSlider.setToolTipText(bundle.getString("mosaic.slider.tooltip")
); // NOI18N
mosaicTileCountSlider.addChangeListener(new
javax.swing.event.ChangeListener() {
public void stateChanged(javax.swing.event.ChangeEvent evt) {
mosaicTileCountSliderStateChanged(evt);
}
});
jLabel12.setText("Number of tiles:");
jLabel12.setVerticalAlignment(javax.swing.SwingConstants.BOTTOM);
mosaicImageLable.setBackground(new java.awt.Color(255, 255, 255));
mosaicImageLable.setHorizontalAlignment(javax.swing.SwingConstants.CENT
ER);
mosaicImageLable.setBorder(new javax.swing.border.LineBorder(new
java.awt.Color(0, 0, 0), 1, true));
mosaicImageLable.setDoubleBuffered(true);
mosaicImageLable.setHorizontalTextPosition(javax.swing.SwingConstants.CEN
TER);
mosaicImageLable.setMinimumSize(new java.awt.Dimension(260, 260));
buttonMosaicSave.setText("Save result ....");
buttonMosaicSave.setToolTipText(bundle.getString("mosaic.saveButton.tooltip"
));
private void vieMenuStartPageActionPerformed(java.awt.event.ActionEvent evt)
{//GEN-FIRST:event_vieMenuStartPageActionPerformed
((CardLayout) topPane.getLayout()).first(topPane);
}
public static void main(String args[]) {
java.awt.EventQueue.invokeLater(new Runnable() {
public void run() {
new MainFrame().setVisible(true);
54

}
});
}
55

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