CONTENT-BASED IMAGE RETRIEVAL

“A picture speaks more than a thousand words !!”

Presented By:

D.SRIKANTHV.M.SRI KRISHNAG.SRIRAMB.ABHILASH

2

INTRODUCTION

3

INTRODUCTION

Image Retrieval system for retrieving images from large database of digital images

Common method of image retrieval utilizes metadata / keywords

Manual image annotation is time consuming

Locating desired image from small database is possible, where as in large database more effective techniques are needed

4

EXISTING SYSTEM

QBIC supports users to retrieve image by colour, shape and texture

QBIC provides several query methods Simple Query Mutli-Feature Query Mutli-Pass Query

5

EXISTING SYSTEM

Photo Book system supports users to retrieve image by colour, shape and texture

Photo Book provides set of matching algorithms, divergence, vector space angle, histogram and Fourier peak

6

PROPOSED SYSTEM

Currently most widely used image search engine is GOOGLE. It provides its users with textual annotation. Not many images are annotated with proper description so many relevant images go unmatched

CBIR uses Quadratic Distance & Integrated Regional Matching (I.R.M)

Quadratic Distance yield metric distance IRM is non-metric and gives result that are not optimal

7

PROPOSED SYSTEM

Our proposed system uses modified IRM and colour feature which overcomes above mentioned disadvantages

We also provide an interface where user can give query images as input, automatically extracts the colour feature and compared with the images in database, retrieve the matching image

8

HARDWARE REQUIREMENTS

System Configuration:

Pentium III Processor with 700 MHz Clock Speed

256 MB RAM 20 GB HDD, 32 Bit PCI Ethernet Card.

9

SOFTWARE REQUIREMENTS

Operating System

Windows NT/2000 (Client/Server).

Software requirements

Java, JDK 1.4, J2SDK 1.4, Swings, RMI and Java Network Programming.

10

MODULES

11

MODULES

ADMINISTRATOR MODULEADMINISTRATOR MODULE

USER MODULEUSER MODULE

SEARCHING MODULESEARCHING MODULE

12

ADMINISTRATOR MODULE

Maintaining the image database.

Update the database according to the users request.

Classify the images for efficient searching.

13

USER MODULE

Upload the query images.

14

SEARCHING MODULE

Searching based on a given image.

Integrate the search with the existing application.

Combine querying techniques with content independent metadata.

15

IMAGE FEATURES

• Texture (Laws, Gabor filters, local binary partition)

• Color (histograms, grid layout, wavelets)

• Shape (first segment the image, then use statistical or structural shape similarity measures)

• Objects and their Relationships

16

IMAGE FEATURE / HISTOGRAMS

Image Database

Query Image

Colour Measure

Retrieved Images

Histogram

User

ComparisonImages

17

TIGER IMAGE AS A COLOUR GRAPH

sky

sand

tiger grass

aboveadjacent

above

inside

above aboveadjacent

image

abstract regions

18

Global Shape Properties:Tangent-Angle Histograms

135

0 30 45 135

19

Gridded Colour

Gridded colour distance is the sum of the color distancesin each of the corresponding grid squares.

1 12 2

3 34 4

20

Object Detection: Rowley’s Face Finder

1. Convert to gray scale2. Normalize for lighting3. Histogram equalization4. Apply neural net(s) trained on 16K images

32 x 32 windows ina pyramid structure

21

UML DIAGRAMS

22

CLASS DIAGRAM

INPUT

package image rawimj1integer : package_imagecolomns1integer : package_imagerows1package_image_tracker1integer : package_pix1integer : package_pix3integer : filenofloat : he1string : str

public void main string()package input()

HISTOGRAM

integer : imgnostring : imgnamefloat : he1

public histogram()

DISPLAY

private : thread imageprivate : imagetodisplayptivate : imagearrayinteger : noimgsinteger : currentimageinteger : sleeptimeinteger : imgcols1integer : imgrows1integer : pix1integer : pix3float : hesfloat : hes1integer : fileno1integer : ninteger : linteger;kinteger : mstring : str1string : str2string : str3string : str0integer : xinteger : y

void init()void start()void suspend()void destroy()void run()void paint()void input123()

23

USE CASE DIAGRAM

query image

visual content description

feature vector

similarity comparsion

retrieval result

feature dabase

includes

DBA

visual content description

user

image database

24

SEQUENCE DIAGRAM

User SimilarityFeature VectorVisual ContentImage Result

Query Image()

Description()

Feature Vector()

Compare Similarity()

Retrive Result()

USER

25

SEQUENCE DIAGRAM

DBA DBA SimilarityDatabaseVisual ContentImage Result User

Create image Database()

Visual Content Description()

Feature Database()

Includes()

Retrive result()

User()

26

HOME PAGE

27

HOME PAGE

28

HOME PAGE

29

HOME PAGE

30

CONCLUSION

31

CONCLUSION

Satisfactory progress

It’s easy to compute.

It’s more stable than the color histogram, QBIC, Photo Book methods.