Acropolis Institute of Technology and Research

Department of Computer Science and Engineering

Content Based Image Retrieval

Software Requirement Specifications

Submitted to: Submitted by:

Ms. Mamta Sakpal Prakhar Jain (67)

Ms. Sushma Khatri Pranjal Solanki(69)

Ms. Preeti Jain Prerna Sisodia(72)

Mr. Raman Bhati

Table of Contents

1. Introduction

1.1 Purpose 1.2 Scope 1.3 Definitions, Acronyms, and Abbreviations 1.4 References 1.5 Overview

2. The Overall Description

2.1 Product Perspective 2.1.1 System Interfaces 2.1.2 Interfaces 2.1.3 Hardware Interfaces 2.1.4 Software Interfaces 2.1.5 Communications Interfaces 2.1.6 Memory Constraints 2.1.7 Operations 2.1.8 Site Adaptation Requirements

2.2 Product Functions 2.3 User Characteristics 2.4 Constraints 2.5 Assumptions and Dependencies 2.6 Apportioning of Requirements

3. Specific Requirements

3.1 External interfaces 3.2 Functions 3.3 Performance Requirements 3.4 Logical Database Requirements 3.5 Design Constraints

3.5.1 Standards Compliance

3.6 Software System Attributes

3.6.1 Reliability 3.6.2 Availability 3.6.3 Security 3.6.4 Maintainability 3.6.5 Portability

3.7 Organizing the Specific Requirements

3.7.1 System Mode 3.7.2 User Class 3.7.3 Objects 3.7.4 Feature 3.7.5 Stimulus 3.7.6 Response 3.7.7 Functional Hierarchy

4. Change Management Process 5. Document Approvals 6. Supporting Information

1. Introduction

1.1 Purpose

In last few years the potential growth in digitization of images has occurred,with immense amount of information flowing and stored in the database of world wide web.the No. of users exploiting the WWW has increased tremendously while accessing and manipulate remotely-stored images in all kinds of new and exciting ways.However, they are also discovering that the process of locating a desired image in a large and varied collection can be a source of considerable frustration.The problems of image retrieval are becoming widely recognized, and the search for solutions an increasingly active area for research and development.Some indication in form of No. of research and development in field of CBIR.No.of journal articles,research papers, appearing each year on this subject.

Traditional way to search image in database is to create a textual description of all the images in the database and use the methods from text-based information retrieval to search based on the textual descriptions.Unfortunately, this method is not feasible. On the one handannotating images has to be done manually and is a very time-consuming task and on theother hand images may have contents that words cannot convey.

This has given rise in interest of techniques for retrieving images on the basis of automatically-derived features such as colour, texture and shape – a technology now generally referred to as Content-Based Image Retrieval (CBIR)

1.2 Scope

The software product is content based image reteival (CBIR) is about developing an image search engine, not only by using the text annotated to

the image by an end user (as traditional image search engines), but also using the visual contents available into the images itselves.Initially, CBIR system should has a database, containing several images to be searched. Then, it should derive the feature vectors of these images, and stores them into a data structure like on of the “Tree Data Structures” (these structures will improve searching efficiency).A CBIR system gets a query from user, whether an image or the specification of the desired image. Then, it searchs the whole database in order to find the most similar images to the input or desired image.

CBIR usually deals with large image collection of low level and high level features,which directly influence indexing and retival complexity,memorey and disk space requirment.due to high memorey and processing power requirment,cbir has not widely been appplied on platforms having limited resouces,such as mobile devices

1.3 Definitions, Acronyms, and Abbreviations.

CBIR: content based image retrieval QBIC: query by image content CBVIR :content-based visual information retrieval

definitions:

● A feature vector ~vˆI of an image can be thought of as a point in Rn space: ~vˆI =

(v1, v2, ..., vn), where n is the dimension of the vector.Examples of possible feature vectors are a color histogram [14], a multiscale fractalcurve [15], and a set of Fourier coefficients [16]

● The row mean vector is the set of averages of the intensityvalues of the respective rows.

● The column mean vector is the set of averages of the intensity values of the respective

columns.● kekre’s transform:

● Euclidean distance ~ the ordinary distance between two points that one would measure with a ruler, and is given by the pythagorous formula.

●

Key words:

CBIR, Image Splitting, Energy Compaction, Kekre Transform,Row feature vector, Column feature vector

1.4 References

● H.B.Kekre, Sudeep D. Thepade, “Scaling Invariant Fusion of Image

Pieces in Panorama Making and Novel Image Blending Technique”, International Journal on Imaging

(IJI), Autumn 2008, Volume 1, No. A08, Available online at www.ceser.res.in/iji.html (ISSN: 0974-0627).

● H.B.Kekre, Sudeep D. Thepade, “Color Traits Transfer to Grayscale Images”, IEEE –Int. Conference on Emerging Trends in Engineering and Technology, ICETET-2008, 16-

18 July 2008, Raisoni College of Engineering, Nagpur.● E. Saber, A.M. Tekalp, ”Integration of color, edge and texture

features for automatic region-based image annotation and retrieval,” Electronic Imaging, 7, pp. 684–700, 1998.

● H.B.Kekre, Tanuja Sarode, Sudeep D. Thepade, “Color-Texture Feature

based Image Retrieval using DCT applied on Kekre’sMedianCodebook”, International Journal on Imaging (IJI), Volume 2, NumberA09, Autumn 2009,pp. 55-65. Available online atwww.ceser.res.in/iji.html (ISSN: 0974-0627).

● R. W. Picard and T. P. Minka, “Vision texture for annotation,” J. Multimedia Syst., vol. 3, no. 1, pp. 3–14, 1995.

● S. Santini and R. Jain, “Similarity measures,” IEEE Trans. PatternAnal.Mach. Intell., vol. 21, no. 9, pp. 871–883, Sep. 1999.

1.5 Overview

The software product is content based image reteival (CBIR) the complete search engine that retrieves images based on content like colour ,texture,shape,another image etc.. Rest of the SRS is organized considering the requirements of multiple connected systems in mesh architecture. SRS of cbir describes all the requirements in such a manner that it can be easily understood. It describes functional as well as non functional requirements. Specific requirements contains design constraints, data base requirement, Standards Compliance etc.

2. The Overall Description

2.1 Product Perspective

This product is independent and self contained which includes folllowing components:

● Feature Extractor● Creates the metadata● Query Engine● Calculate similarity● User Interface

Feature Extractor:- This components deals with calculation of feature vector of an image which represents an image in Compressed matrix form.The image is given as an input to an algoritmwhich genrates an unique vector for that image.

Creates the metadata:-when an image is given to an algorithm its all feature vectors of color shape and texture are tagged with that image which acts as a metadata for our image database.

Query engine: this component takes query image as input and sends image to feature extractor

Calulates similarity: this component calculate simalirity between query image and image database.

User interface: this is the GUI for displaying retived images from datasase as a result.this is form on which users will intreact with our system.

2.1.1 System Interfaces

It represents a system that evaluates Content-Based Image Retrieval (CBIR) engine for vector images, by a graphical interface providing query-by-example interaction with query output result, and analysis of result quality. To support requirements of different application domains, the engine offers different metrics for similarity computation. The graphical interface offers tools that helps in the selection of criteria and parameters necessary to tune the system to a specific application domain.

2.1.2 Interfaces

our system will interact with users with the help of user friendly GUI.The GUI will be self contained and added with Help function for initial users.

2.1.3 Hardware Interfaces

The content based image retrieval (CBIR) does not require any external hardware requirements apart from a mouse and keyboard that would facilitate giving proper inputs in the form of images.it may also require a scanner to scan external images like medical x-rays .

2.1.4 Software Interfaces

Matlab 2007-

Digital image processing toollbox & Wavelet toolbox

for the various feature vector metrics generation for CBIR version matlab 7.1Scilab 4.1.2intel core 2 duo processor

2.1 GHZ,2 GB RAM

C# for devolping GUI of softwareversion 3.0

SQL SERVER 2007for Database of images

Purpose :Mtalab used for the various feature vector metrics generation for CBIR SQL SERVER 2007 for Database of images

2.1.5 Communications Interfaces

2.1.6 Memory Constraints

Due to involvement of high resolution graphical images and larger database of images .the minimum memory required to run the algorithm successfully is 2Gb.

2.1.7 Operations

Image retrieval is the task of searching for images from an image database. The query to thedatabase can be of various types as depicted.

● Query-by-text: The user gives a textual description of the image he is looking for.

● Query-by-sketch: The user provides a sketch of the image she is looking for.

● Query-by-example: The user gives an example image similar to the one he is looking for.

The interface allows a user to specify a query by means of a query pattern and to visualise the retrieved similar images .

2.1.8 Site Adaptation Requirements

A computer equipped with matlab and internet connection are the only site adaptation requirements for cbir.

2.2 Product Functions

The CBIR software system will perform effectively the following functions1. if any example query image is provided it searches and extracts the images that are in complete correlation with the query image2. if no query image is available ,the user can provide information about the contents of the image for eg: a red car. the contents can be classified into following broad categories

● colour queries either by sliders varying the relative amounts of red, green and blue in the query , or by selecting a desired colour from a palette.

● shape queries ● texture queries could also be specified by choosing from a

palette ● handmade figures by sketching the desired object on the screen

2.3 User Characteristics

The users are assumed to have basic knowledge of the computers and Internet browsing.the cbir is a general purpose application which does not have any specific user .anybody can efficiently operate the system application.

2.4 Constraints

Hardware limitation: cbir requires large memory requirements approximately 2 GBsignal timing requirements: the cbir system requires a large processing time for generating output images.

2.5 Assumptions and Dependencies

the cbir system has several hardware requirements and memory requirements .if any of the resources required mentioned in srs are not available the system will not give presicive results.

2.6 Apportioning of Requirements.

the CBIR systems so far can be used in artifical intelligence(robotics).in future there may be the requirements of highly effective algorithms that enable fast retrieval if images from even very large databases

3. Specific Requirements

The system shall contain ● a browse tool box that will enable the user to select images from his

own personal database,thereby enabling the query by example feature of CBIR.

the system may contain● a paint box thereby enabling users to draw the sketches and the

software will search similar images.the system should contain

● a colour pallette for the users to select specific images based on a range of colors

● a text based text box which will select all those images tagged with that particular text .

● a facility that enables multiple selection of the above functions such as color:red as well as some query image.

3.2 Functions

sequence of operations:

● feature extraction of query image● image comparision● image retreival

FEATURE EXTRACTION:The feature is defined as a function of one or more measurements, each of which specifies some quantifiable property of an object, and is computed such that it quantifies somesignificant characteristics of the object. We classify the various features currently employed asfollows:• General features: Application independent features such as color, texture, and shape. According to the abstraction level, they can be further divided into: - Pixel-level features: Features calculated at each pixel, e.g. color, location.- Local features: Features calculated over the results of subdivision of the image band on image segmentation or edge detection. - Global features: Features calculated over the entire image or just regular sub-area of an image.• Domain-specific features: Application dependent features such as human faces, fingerprints, and conceptual features.These features are often a synthesis of low-level features for a specific domain.

● Feature extraction is done in the following way, image for which the feature vector is to be

calculated is splited into R, G and B color components and for each of these three components row and column mean vectors are computed over which Kekre’s transform is applied toobtain the coefficients to form the feature vectors (RRK, RCK GRK, GCK and BRK, BCK for R, G and B planes respectively and a feature database FDB1 is formed for alldatabase images

A color histogram H for a given image is defined as a vector H = {h[1], h[2], . . . h[i], . . . , h[N]} where i represents a color in the color histogram, h[i] is the number of pixels in color i in that image, and N is the number of bins in the color histogram, i.e., the number of colors in the adopted color model. In order to compare images of different sizes, color histogramsshould be normalized. The normalized color histogram H is defined for h[i] ‘= h[i]/XY

where XY is the total number of pixels in an image

● For comparing the similarity of the query image with the database image we use a parameter called EUCLIDEAN DISTANCE

● Finally to access the retrieval effectiveness and performance of the technique we use the PRECISION and RECALL as statistical comparison parameter for our proposed technique of CBIR.

BASIC FORMULAES THAT MAY BE USED:● Precision=number of relevant images retrieved / total no of images

retrieved● Recall=no of relevant images retrieved / total no of relevant images in

database● euclidean distance is given by●

3.3 Performance Requirements

to access the retrieval effectiveness and performance of the technique we use the PRECISION and RECALL as statistical comparison parameter for our proposed technique of CBIR.

● Precision=number of relevant images retrieved / total no of images retrieved

● Recall=no of relevant images retrieved / total no of relevant images in database

3.4 Logical Database Requirements

the database will contain● image● row mean and column mean● feature vectors● euclidean distance● precision and recall

3.5 Design Constraints

3.6 Software System Attributes

● Fast searching● scalable● portable● perfect image comparison (calculation of eulers distance)● proper indexing of images in database● feature vectors corresponding to every regions of every images

recursively to form the hierarchy of the indexing structure.● robustness:The color histogram is invariant to rotation of the image

on the view axis, and changes in small steps when rotated otherwise or scaled . It is also insensitive to changes in image and histogram resolution and occlusion.

● Effectiveness. There is high percentage of relevance between the query image and the extracted matching images.

● Implementation simplicity. The construction of the color histogram is a straightforward process, including scanning the image, assigning color values to the resolution of the histogram, and building the histogram using color components as indices.

● retrieval factor and indexing parameters.

3.6.1 Reliability

Reliability is the probability that a device will perform its intended function during a specified period of time under stated conditions.

3.6.2 Availability

the cbir system has high value of availability, as it is the most platform friendly product.It is mostly constructed for windows systems. today these systems are mostly made available on the web for universal usage.

3.6.3 Computational simplicity.:The histogram computation has O(X, Y ) complexity for images of size X × Y . The complexity for a single image match is linear, O(n), where n represents the number of different colors, or resolution of the histogram.

3.6.4 storage requirements. The color histogram size is significantly smaller than the image itself, assuming color quantisation.so storage requirement is limited

3.6.4 Maintainability

our product can be maintained in order to:● correct defects● meet new requirements● make future maintenance easier, or

● cope with a changed environment● also maintainance of large image database is required so that they

can be retrieved fastly and easily

4. Change Management Process

5. Document Approvals

6. Supporting Information