Project Presentation Image Processing

8/15/2019 Project Presentation Image Processing

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Under the guidance of..Dr.k.GiribabuHead of the department

Electronics & Communication Engineering

By.... T. ounica. !ukanya

". Go#thami. Bha$ya

Content Based Image Retrieval(CBIR)


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CONTENTS INTRODUCTION

IMAGE RETRIEVA ARC!ITECTURE

"!# CBIR$$$$$

CBIR MODE

!O" CBIR "OR%S$$$$

&EATURES O& IMAGE

A''ICATIONS

CONCUSION


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RODUCTION

ontent based image retrieval, a technique which uses visual

contents from large scale image database according to users

query, has been an active and fast advancing research area

since the 1990s.

n our project we concentrated on regionhistogram features toetrieve the images according to an e!ample query image

upplied by the user.

e ha$e proposed a CB% based image retrie$al system'#hich analyses innate properties of an image such as' thete(ture' and histogram for e)cient and meaningfulimage retrie$al.


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IMAGE RETRIEVA ARC!ITECTURE

%mage

collection

"isual

features

Te(t

annotations

ulti dimensional inde(ing

*uery processing

*uery interface

+eaturee(traction

user


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MET!ODS INVOVED

Tet *ased image retrieval (TBIR)

Content *ased image retrieval (CBIR)


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"!# CBIR$$$$$$

Contentbased means that the search analyses the contents of the image not the metadata such as "eywords, labels or

tags associated with the images.

It is also "nown as query by image content #$%IC& and

contentbased visual information retrieval #C%'I(&

)eatures such as colour , te!ture, shape and spatial are retrieved

automatically

*imilarities of the images are based on distance between the features

+o need of domain e!perts

escription of image in te!t form doesn-t required


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CBIR MODE

Fig: Block diagram of CBIR system


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!O" CBIR "OR%S$$$$$$


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&EATURES O& IMAGE

Colo+r

S,a-e

Tet+re


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COOR

Color similarity is achieved by computing a color histogram for each image that identifies the

proportion of pi!els within an image holding specific values

#that humans e!press as colors&.

!amining images based on the colors they contain

is one of the most widely used techniques because it

does not depend on image si/e or orientation.

Color searches will usually involve comparing

color histograms, though this is not the only technique in

practice.

http://en.wikipedia.org/wiki/Color_histogramhttp://en.wikipedia.org/wiki/Color_histogram


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S!A'E

*hape does not refer to the shape of an image but to the

shape of a particular region that is being sought out.

*hapes will often be determined first applying segmentatio

or edge detection to an image.

ther methods li"e use shape filters to identify given shapes of an image.

http://en.wikipedia.org/wiki/Segmentation_(image_processing)http://en.wikipedia.org/wiki/Edge_detectionhttp://en.wikipedia.org/wiki/Edge_detectionhttp://en.wikipedia.org/wiki/Segmentation_(image_processing)


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TE.TURE

e!ture measures loo" for visual patterns in images and

how they are spatially defined.

hese sets not only define the te!ture, but also where in

the image the te!ture is located.

e!ture is a difficult concept to represent. he identificatio of specific te!tures in an image is achieved primarily by

modeling te!ture as a twodimensional gray level variation.


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ICATIONS

edi/al A--li/ations

ood e(ample of the po#er of image retrie$al is that of dist

dicine

e* A--li/ations

d to retrieve digital images from the large database search for oneeci-c image

arch for a picture to go #ith a broad story or search to illustdocument.


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Project Presentation Image Processing

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