drishti report

8/7/2019 DRISHTI report

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1. Abstract:

Pair wise similarity or comparison generally refers to any process of comparing entities in

pairs to judge which of each pair is preferred , or has a greater amount of some quantitative

property . The method of pair wise comparison is used in the scientific study

of preferences, voting systems, intelligent systems or AI systems. Extending the idea of

Learning, a measure of similarity between pairs of objects is learnt which is a fundamental

problem in machine is learning. Our goal is to learn the similarity between images in pairs.

This knowledge of similarity can be used especially for storage and retrieval of images in

databases. The same knowledge so obtained can be used to prepare clusters. Several other

practical usages exist to make use of measures learned. The practical usage of similarity

between images is for storage and retrieval in image (multimedia) databases, facerecognition, computer vision and so on.

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http://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Voting_systemshttp://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Voting_systems


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2. Introduction

Learning a measure of similarity between pairs of objects is a fundamental problem in

machine learning. Pair wise similarity generally refers to any process of comparing

entities in pairs to judge which of each pair is preferred , or has a greater amount of

some quantitative property. Machine learning is a scientific discipline that is concerned

with the design and development of algorithms that allow computers to evolve behaviours

based on empirical data , such as from sensor data or databases . A learner can take

advantage of examples (data) to capture characteristics of interest of their unknown

underlying probability distribution. Data can be seen as examples that illustrate relations

between observed variables. A major focus of machine learning research is to

automatically learn to recognize complex patterns and make intelligent decisions basedon data; the difficulty lies in the fact that the set of all possible behaviours given all

possible inputs is too large to be covered by the set of observed examples (training data).

Hence the learner must generalize from the given examples, so as to be able to produce a

useful output in new cases. Machine Learning when applied to large databases is called

Data Mining, when applied to images it has applications like face recognition,

segmentation and object-based image similarity.

Google Search Engine is a very nice idea. It provides information behind the scene byauto completion of text query and gives the most visited (highest ranked) result first.

Here we provide an application DRISHTI which can be used to define considerable

image similarity and hence can be used to search for semantically similar image(s) from a

image set.

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http://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Algorithmhttp://en.wikipedia.org/wiki/Computerhttp://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Databasehttp://en.wikipedia.org/wiki/Preferencehttp://en.wikipedia.org/wiki/Quantitative_propertyhttp://en.wikipedia.org/wiki/Algorithmhttp://en.wikipedia.org/wiki/Computerhttp://en.wikipedia.org/wiki/Datahttp://en.wikipedia.org/wiki/Sensorhttp://en.wikipedia.org/wiki/Database


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2.1 Objective:

a. Study of image similarity approaches and algorithms.

b. Defining a similarity measure for images.

c. Design of an application model of above study.

d. To display the result set in as per the descending relevance to query image.

e. Eliminating of identical images from result set generated by application model.

2.2 Scope:

Every system in the world is moving towards automation and intelligence. True intelligence

is achieved only when a system starts to learn by itself, when it starts to draw conclusions

from its experience. Same is we wanted for a system which could study the similarity of

image. The application that evolved from this is not meant for the general purpose user but

scope will be in specific fields like clustering of images, face recognition.

2.3 Problem in existing system:

The study of image similarity is advanced field which is still one of the most dominating

research field. No generic approach for study of image exists. No system or approach exists

which works on all categories of images. The heterogeneity in images is from image formatsto colour models even from random pixels patterns to their colours. Some approaches exists

which works for some image sets, but do not scale to large image sets. The main reason being

the slow processing of image data, internal representation of images takes a lot of physical

memory.

This work is motivated by a research paper which aims at scaling the similarity learning in

images to large sets. This done by three important hits-

a. Similarity Learning

b. Sparse representation of images

c. PA algorithmic approach

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2.4 Platform:

2.4.1 Hardware Specification:

Table 1

2.4.2 Software Specification:

Table 2

Implementation Language: The implementation language used is MATLAB .

MATLAB is a high-level, fourth generation language and interactive environment that

enables programmer to perform computationally intensive tasks faster and easily than with

traditional programming languages such as C, C++, and Fortran. Following are the primary

reasons which led us to use MATLAB

, these are-a. Support for both Procedural and Object-Oriented paradigm support.

b. Platform Independence.

c. Specialised package for image processing

d. Database toolbox for database maintenance

3. System Requirement Analysis:

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S.No. Description Alternatives (If available)

1. 1 Gb of RAM (for best performance) Not Applicable

2. Processor Pentium-4 or above Not Applicable

S.No. Description Alternatives (If available)

1. Windows Family

(Xp, vista, 7)

Linux family

(Ubuntu)2. MS-Access MySQL, Derdy,Oracle,Ingress

3. Matlab 7.0 Not-Applicable

4. Environment Matlab Runtime Environment


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3.1 Information Gathering:

Information Gathering refers to the act of understanding the nature of program to develop

an application. In our project information gathering mainly includes analyzing different

standalone software for better and friendly interface and also in order to gather information about different fields to design the system. It mainly includes

a. About Functionalities

b. About Database

c. Understanding Information Domain

a. About Functionalities: Here we analyze the functionalities which may not be

important for a general user but are crucial from learning point of view.

b. About Database: Data regarding different entities considering relationships has

been gathered. Database will be designed accordingly.

c. Understanding Information Domain: Proper understanding of information domain

will lead to a proper flow of information from database to user to & fro by appropriate

functionalities.

3.2 System Feasibility:

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3.2.1 Economical Feasibility: Economic feasibility check is an evaluation process carried

out to determine development cost and profit incurred after deployment.

In our project:

a. Software: The software used in developing this project is either provided by

the college or are provided as freeware by software websites or are cracked or are made

available for students.

b. Hardware: The configuration cost for the system is negligible as all the

hardware needed and also internet connectivity is already being provided to us by the college.

3.2.2 Technical Feasibility: Technical feasibility of this system mainly depends on thesimilarity evaluation algorithm and parameters

a. Language MATLAB

b. Database Tool MS-Access

3.2.3 Behavioral Feasibility: Our project is mostly a computational application . It is

practically not possible to achieve the accuracy at finest level but the project can be done

within given time period.

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4. System Analysis:

4.1 Information Flow Representation:

4.1.1 ER Diagram:

The entity-relationship (E-R) data model perceives the real world as consisting of basic

objects, called entities, and relationships among these objects. It was developed to facilitate

database design by allowing specification of an enterprise schema, which represents the

overall logical structure of a database. The E-R data model is one of several semantic data

models; the semantic aspect of the model lies in its representation of the meaning of the data.

The E-R model is very useful in mapping the meanings and interactions of real-world

enterprises onto a conceptual schema. Because of this usefulness, many database-design tools

draw on concepts from the E-R model.

FIGURE 1. ER-DIAGRAM

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Admin Image

Img_id

Img_size

Img_type

Img_name

Img_date

reg

ulates

password


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4.1.2 Class Diagram:

A class diagram is a type of static structure diagram that describes the structure of a system

by showing the system's classes, their attributes, and the relationships between the classes.

Class diagrams are widely used to describe the types of objects in a system and their relationships. Class diagrams model class structure and contents using design elements such

as classes, packages and objects. Class diagrams describe three different perspectives when

designing a system, conceptual, specification, and implementation. These perspectives

become evident as the diagram is created and help solidify the design

Classes are composed of three things: a name, attributes, and operations. Below is an

example of a class.

FIGURE 2. CLASS DIAGRAM FORMAT

Class diagrams also display relationships such as containment, inheritance, associations and

others. The association relationship is the most common relationship in a class diagram. The

association shows the relationship between instances of classes. For example, the class Order

is associated with the class Customer. The multiplicity of the association denotes the number

of objects that can participate in then relationship. Another common relationship in class

diagrams is a generalization. A generalization is used when two classes are similar, but have

some differences.

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FIGURE 3. CLASS DIAGRAM

4.1.3 Use Case:9


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A use case diagram is a type of behavioral diagram defined by the Unified Modeling

Language (UML) and created from a Use-case analysis. Its purpose is to present a graphical

overview of the functionality provided by a system in terms of actors, their goals (represented

as use cases), and any dependencies between those use cases. The main purpose of a use case

diagram is to show what system functions are performed for which actors. Roles of the actors

in the system can be depicted.

Use case diagrams depict:

Use cases: A use case describes a sequence of actions that provide something of measurable

value to an actor and is drawn as a horizontal ellipse.

Actors: An actor is a person, organization, or external system that plays a role in one or more

interactions with your system. Actors are drawn as stick figures.

Associations: Associations between actors and use cases are indicated in use case diagrams

by solid lines. An association exists whenever an actor is involved with an interaction

described by a use case. Associations are modelled as lines connecting use cases and actors

to one another, with an optional arrowhead on one end of the line. The arrowhead is often

used to indicating the direction of the initial invocation of the relationship or to indicate the

primary actor within the use case. The arrowheads are typically confused with data flow and

as a result I avoid their use.

FIGURE 4. USE-CASE DIAGRAM

4.1.4 Sequence Diagram:

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4.1.4.1 Search Image:

FIGURE 5. SEQUENCE DIAGRAM for Search Image Use case

4.1.4.2 See Results

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FIGURE 6. SEQUENCE DIAGRAM for See Results Use case

4.1.4.3 Discard results:

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FIGURE 7. SEQUENCE DIAGRAM for Discard Results Use case

4.1.4.4 Update-dataset:

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FIGURE 8. SEQUENCE DIAGRAM for update dataset Use case

4.1.5 Activity Diagram

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An activity diagram is a diagram that shows activities and actions to describe workflows. In

the Unified Modeling Language an activity diagram represents the business and operational

step-by-step workflows of components in a system. An activity diagram shows the overall

flow of control.

Construction

Activity diagrams are typically used for business process modeling. They consist of:

Initial node.

Activity final node.

Activities

The starting point of the diagram is the initial node, and the activity final node is the ending.

An activity diagram can have zero or more activity final nodes. In between activities are

represented by rounded rectangles.

FIGURE 9. ACTIVITY DIAGRAM

5. Design:

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Actor: Those entities (people or devices) that interact with the target system by

producing or consuming information that is necessary for requisite processing.

The actor for our system is the administrator and general user.

FIGURE 10. Architectural Context Diagram

5.1.2 Architectural behavioural diagram: Behaviour diagrams emphasize what must

happen in the system being modelled:

a. State machine diagram: standardized notation to describe many systems, from

computer programs to business processes.

b. Use case diagram: shows the functionality provided by a system in terms of actors,

their goals represented as use cases, and any dependencies among those use cases.

5.1.3 Description of Architectural design: Architectural patterns are software patterns that

describe solutions known to work efficiently, to architectural problems in software

engineering. It gives description of the elements and relation type together with a set of

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DATABASE PASTREFERENCES

DRISHTI

RESULT SETSIMILARITYMARKINGMODULE

ADMINUSER


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constraints on how they may be used. An architectural pattern expresses a fundamental

structural organization schema for a software system, which consists of subsystems, their

responsibilities and interrelations. In comparison to design patterns, architectural patterns are

larger in scale.

One of the most important aspects of architectural patterns is that they embody different

quality attributes. For example, some patterns represent solutions to performance problems

and others can be used successfully in high-availability systems. In the early design phase, a

software architect makes a choice of which architectural pattern(s) best provide the system's

desired qualities. Some architectural patterns are implemented in software frameworks which

can be used to more quickly build specific programs.

The architectural design of a standalone system is often characterized, as communication

between the database server and performing computations, in the following way:

The goal is to achieve the quality of scalability. A database server exists to provide the image

set also called training data. The user originates a call to the database server y a query image,

which is then searched for similar images, synchronously or asynchronously.

The main aspects that are considered during architectural design are:

a. The primary objective of architectural design is to develop a modular program

structure and represent the control relationships between modules.

b. Secondly, architectural design melds program structure and data structure, defining

interfaces that enable data to flow throughout the program.

5.1.4 Control Hierarchy:

Control hierarchy, represents the organization of program components (modules) and implies

a hierarchy of control. The most common notation is the three linked diagram that represents

the hierarchical control for call and return architectures. Depth and width provide an

indication of the number of levels of control and overall span of control, respectively. Fan-out

is a measure of the number of modules that are directly controlled by another module. Fan-in

indicates how many modules directly control a given module.

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FIGURE 11. Control Hierarchy Diagram

5.2 Modular Approach:

Modularity is generally desirable, especially in large, complicated programs. Inputs are

usually specified syntactically in the form of arguments and the outputs delivered as return

values. Scoping is another technique that helps keep procedures strongly modular. It prevents

the procedure from accessing the variables of other procedures (and vice-versa), including

previous instances of itself, without explicit authorization. Less modular procedures, often

used in small or quickly written programs, tend to interact with a large number of variables in

the execution environment, which other procedures might also modify.

Because of the ability to specify a simple interface, to be self-contained, and to be reused,

procedures are a convenient vehicle for making pieces of code written by different people or

different groups, including through programming libraries.

5.2.1 Modules Used:

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Authenticate

Search images

Updateimage set

Show result set Discard result set

QUIT


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Modules are typically incorporated into the program through interfaces. The various modules

used in our application are as follows:

a. Home page: The home page of the application is also the query page, which includes

the functionalities provided to user as well as admin.

5.2.2 Internal Data Structure: We have employed primitive data types to hold the data for

loops, counters. Images which is the external data, is represented in application using

matrices. Not as the ordinary matrix but as sparse matrix.

5.2.3 Algorithm Design for Operations:

The application so designed to learn the issue so called pair wise similarity in images is a

two-tier application, which has an user-level interface and a database holding images.

algorithm:

1. User enters the query.

2. Application program analyzes the query image.

3. Comparison with other image or images metadata-templates.

4. Create the result set

5. Display the result to user.

5.3 Data Design:

The data design creates the model of data and information i.e. represented at a high level of

abstraction. The data objects defined during software requirements analysis are modelled

using ER diagrams. The data design activity translates these elements of requirement model

into data structure at component data level.

1. Admin entity:

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FIGURE 12. Admin Entity

2. Image entity:

FIGURE 13. Image entity

5.4 Interface Design:

The interface design is the bridge for interaction between a human and a computer .It creates

an effective communication between the human and the computer .The interface design

begins with the identification of the user, task and environmental requirements .It is theinformation representation of the available data thus, if the representation is confusing or

misleading users may misunderstand the meaning of the information. The best possible

representation of the data or the easiest available interface is the Graphical User Interface

(GUI). The GUI provides us with windows, icons, menus, pointers etc. The basic interface

design principles are:

a. User familiarity:

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The interface in this project is easily understood by the user that is the

interface has the user friendly terms. In our project we have used JSP ,

HTML to build various Intranet interfaces all arranged in the page so as

to make the user much comfortable.

b. Consistency:

The project if requirement specific needs to be consistent and so is this

project which satisfies the consistency criteria as a whole. It does not

break down if any unconditional problem is there.

c. Minimal surprise:

The user must not be surprised by the systems behaviour .The system

must be capable of handling the abrupt data. Even if the system shuts

down that should not be abrupt. No abrupt switching off or anomalous

behaviour is there with the system.

5.4.1 Human-machine Interface Design specification: In the industrial design field

of human-machine interaction , the user interface is (a place) where interaction between

humans and machines occurs. The goal of interaction between a human and a machine at the

user interface is effective operation and control of the machine, and feedback from the

machine which aids the operator in making operational decisions. Examples of this broad

concept of user interfaces include the interactive aspects of computer operating systems ,

hand tools , heavy machinery operator controls. and process controls. The design

considerations applicable when creating user interfaces are related to or involve such

disciplines as ergonomics and psychology .

A user interface is the system by which people ( users ) interact with a machine . The user

interface includes hardware (physical) and software (logical) components. User interfaces

exist for various systems , and provide a means of:

a. Input , allowing the users to manipulate a system, and/or

b. Output , allowing the system to indicate the effects of the users' manipulation.22
http://en.wikipedia.org/wiki/Industrial_designhttp://en.wikipedia.org/wiki/Industrial_designhttp://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Operating_systemhttp://en.wikipedia.org/wiki/Operating_systemhttp://en.wikipedia.org/wiki/Toolshttp://en.wikipedia.org/wiki/Toolshttp://en.wikipedia.org/wiki/Heavy_machineryhttp://en.wikipedia.org/wiki/Unit_operationhttp://en.wikipedia.org/wiki/Unit_operationhttp://en.wikipedia.org/wiki/Ergonomicshttp://en.wikipedia.org/wiki/Psychologyhttp://en.wikipedia.org/wiki/Psychologyhttp://en.wikipedia.org/wiki/User_(computing)http://en.wikipedia.org/wiki/Interactionhttp://en.wikipedia.org/wiki/Machinehttp://en.wikipedia.org/wiki/Systemhttp://en.wikipedia.org/wiki/Inputhttp://en.wikipedia.org/wiki/Inputhttp://en.wikipedia.org/wiki/Outputhttp://en.wikipedia.org/wiki/Outputhttp://en.wikipedia.org/wiki/Industrial_designhttp://en.wikipedia.org/wiki/Human-computer_interactionhttp://en.wikipedia.org/wiki/Operating_systemhttp://en.wikipedia.org/wiki/Toolshttp://en.wikipedia.org/wiki/Heavy_machineryhttp://en.wikipedia.org/wiki/Unit_operationhttp://en.wikipedia.org/wiki/Ergonomicshttp://en.wikipedia.org/wiki/Psychologyhttp://en.wikipedia.org/wiki/User_(computing)http://en.wikipedia.org/wiki/Interactionhttp://en.wikipedia.org/wiki/Machinehttp://en.wikipedia.org/wiki/Systemhttp://en.wikipedia.org/wiki/Inputhttp://en.wikipedia.org/wiki/Output


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Generally, the goal of human-machine interaction engineering is to produce a user interface

which makes it easy, efficient, and enjoyable to operate a machine in the way which produces

the desired result. This generally means that the operator needs to provide minimal input to

achieve the desired output, and also that the machine minimizes undesired outputs to the

human.

5.4.2 I/O forms:

1. DRISHTI home page

FIGURE 14

2. Selection of query image

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FIGURE 15

6. Limitations:

Following are the limitations so far identified:

a. The application so designed will train the standalone system.b. Not for general user

c. The slow image processing has led to high response time

7. Future Scope: Though we have tried our best to make the project a success but still

there is scope for improvement.

a. Can be extended to a web or distributed application.

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b. Multiple systems can be trained simultaneously.

c. Can be availed to general user for better training and results.

8. Conclusion:

The application would comprise of all the functionalities which are discussed so far. They are

as follows-

a. Train the system to learn the image similarity

b. Search for most relevant images

c. Elimination of duplicity.

9. Bibliography and References: Provided that there are important resources that are

used as references while preparing the report, a complete list of the titles of references

concern must be included.

Help from my project guide Mr. Sapan Prajapati.

From Mr. Preetesh Purohit, our HOD of Computer Science Department.

http://citeseer.ist.psu.edu/ http://en.wikipedia.org/wiki/Machine_learning

http://ai.stanford.edu/~nilsson/mlbook.html

http://en.wikipedia.org/wiki/Matrix_norm

http://www.wisegeek.com/what-does-passive-aggressive-mean.htm

http://www.google.co.in/url?

sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~ela

d/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_A

http://en.wikipedia.org/wiki/Semantic_similarity

An online algorithm for large scale image similarity learning- a research paper.

Other references in the above stated paper.

10. Appendices:

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http://citeseer.ist.psu.edu/http://citeseer.ist.psu.edu/http://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Machine_learninghttp://ai.stanford.edu/~nilsson/mlbook.htmlhttp://ai.stanford.edu/~nilsson/mlbook.htmlhttp://en.wikipedia.org/wiki/Matrix_normhttp://en.wikipedia.org/wiki/Matrix_normhttp://www.wisegeek.com/what-does-passive-aggressive-mean.htmhttp://www.wisegeek.com/what-does-passive-aggressive-mean.htmhttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://en.wikipedia.org/wiki/Semantic_similarityhttp://en.wikipedia.org/wiki/Semantic_similarityhttp://citeseer.ist.psu.edu/http://en.wikipedia.org/wiki/Machine_learninghttp://ai.stanford.edu/~nilsson/mlbook.htmlhttp://en.wikipedia.org/wiki/Matrix_normhttp://www.wisegeek.com/what-does-passive-aggressive-mean.htmhttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://www.google.co.in/url?sa=t&source=web&cd=1&ved=0CBYQFjAA&url=http://www.cs.technion.ac.il/~elad/publications/journals/2006/KSVD_Color_IEEE_TIP.pdf&ei=ypbqTOvQIYzvcJTE-fgK&usg=AFQjCNHEvACt3IRw0MRoqw_8DeiO6Ect_Ahttp://en.wikipedia.org/wiki/Semantic_similarity


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