PRESENTED BY

RAJASEKAR G

3rd MCAMadras University

CONTENT BASED IMAGE RETRIEVAL

(CBIR)

CONTENTS

Clustering Algorithm

Definition

Introduction

Filtering in image

History of image retrieval

Features of image

Conclusion

1 Content-based image retrieval (CBIR) systems are capable to use query for visually related images by identifying similarity between a query Image and those in the image database.

2

3

4

5

INTRODUCTION OF CBIR

The system first extracts and stores the features of the query image then it go through all images in the database and extract the features of each image.

Content Based Image Retrieval (CBIR) is still a research area, which aims to retrieve images based on the content of the query image.

The results are the images that its features are most similar to the query image.

we have proposed a CBIR based image retrieval system, which analyses innate properties of an image such as, the color, texture, and histogram for efficient and meaningful image retrieval.

Definition of CBIR

Definition:

“The process of retrieving images from a collection on the

basis of features (such as colour, texture and shape)

automatically extracted from the images themselves”

Features Of Image

COLOR TEXTURE

SHAPE OTHER RELATED OBJECTS

•Histograms, Gridded layout, wavelets.•Spectrum that covers visible colors : 400 ~ 700 nm•Radiance, Luminance, Brightness

•An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image.•Region based segment•Boundary based Segment

Two dimensional and Three dimensional. External representations(edge and line detection).Internal representations.

Use of the object boundary and its Features (e.g. boundary length)

Results…

MotivationTo efficiently search/retrieve relevant information that people want to use

GoalTo make it easy to search/retrieve/filter/exchange content to maintain archive, and to edit multimedia content etc.

CBIR…

CBIR Image ProcessingImage Retrieval from the image

collections involved with the following steps :

1 Pre-processing

2 Image Classification based on some true factor

3 RGB Components processing

4 Pre-clustering

Cont…

5 Texture feature extraction

6 Similarity comparison

7 Target image selection

8 Target image is retrieved

Block diagram of CBIR

Cont…

Server

Internetor

Intranetor

Extranet

Client

Query Interface

Query byColor Sensation

Query byShape

Learning

Mechanism

Query by

Images

User Drawing

Weight of Features

Query bySpatial Relation

Query byColor

Fectures Extraction

Color Sensation

Color Shape

Spatial Relation

Similarity Measure

Color Sensation

Color Shape

Spatial Relation

Indexing&

Filtering Image Database

Image

Query

Server

History of Image Retrieval

Traditional text-based image search enginesManual annotation of imagesUse text-based retrieval methods

E.g. Water lilies

Flowers in a pond<Its biological name>

Text Based Image Retrieval

by googleby yahoo etc..

Two Classes of CBIR

Narrow

Medical Imagery RetrievalFinger Print RetrievalSatellite Imagery Retrieval

Broad

Photo CollectionsInternet

Narrow vs. Broad Domain

Image Filtering

One of the most important factors that greatly affect the quality of clinical nuclear medicine images is image filtering.Image filtering is a mathematical processing for noise removal and resolution recovery.The goal of the filtering is to compensate for loss of detail in an image while reducing noise.

Filtering Cont…

Mean Filter :Mean filter is the simplest low pass linear

filter. It is implemented by replacing each pixel value with the average value of its neighbourhood. Mean filter can be considered as a convolution filter.

Filtering Cont…

Median Filter:Median filter is a non linear filter. Median

filtering is done by replacing the central pixel with the median of all the pixels value in the current neighbourhood.

Filtering Cont…

Gaussian Filter:Gaussian filter is a linear low pass filter.

A Gaussian filter mask has the form of a bell shaped curve with a high point in the centre and symmetrically tapering

Clustering Algorithm

Clustering is a method of grouping data objects into different groups, such that similar data objects belong to the same group and dissimilar data objects to different clusters.

Image clustering consists of two steps the former is feature extraction and second part is grouping.

Clustering algorithm is applied over this extracted feature to form the group.

Clustering

Applications Areas Of CBIR

Planning and government: there is a lot of satellite imagery of the earth, which can be used to inform important political debates.

Military intelligence: satellite imagery can contain important military information. Typical queries involve finding militarily interesting changes — for example, is there a concentration of force? how much damage was caused by the last bombing raid? what happened today? etc. — occurring at particular places on the earth

Stock photo and stock footage: commercial libraries — which often have extremely large and very diverse collections — survive by selling the rights to use particular images. Effective tools may unlock value in these collections by making it possible for relatively unsophisticated users to obtain images that are useful to them at acceptable expense in time and money.

Access to museums: museums are increasingly creating web views of their collections, typically at restricted resolutions, to entice viewers into visiting the museum. Ideally, one would want viewers to get a sense of what is at the museum, why it is worth visiting and the particular virtues of the museum’s gift store.

Trademark and copyright enforcement: as electronic commerce grows, so does the opportunity for automatic searches to do violations of trademark or of copyright. For example, at time of writing, the owner of rights to a picture could register it with an organization called BayTSP, who would then search for stolen copies of the picture on the web; recent changes in copyright law make it relatively easy to recover fines from violators (see http://www.baytsp.com/index.asp).

Managing the web: indexing web pages appears to be a profitable activity; the images present on a web page should give cues to the content of the page. Users may also wish to have tools that allow them to avoid offensive images or advertising. A number of tools have been built to support searches for images on the web using CBIR techniques. There are tools that check images for potentially offensive content, both in the academic and commercial domains.Medical information systems: recovering medical images “similar” to a given query example might give more information on which to base a diagnosis or to conduct epidemiological studies. Furthermore, one might be able to cluster medical images in ways that suggest interesting and novel hypotheses to experts.

Storing Content of Images

Advantages of CBIR

Retrieving images based on the keywords is not only appropriate, but also time consuming.When compared to TBIR, CBIR is very effective and appropriate.

Focused on effective FEATURE representation such as color, texture, shape.

Easy to retrieve image databases.

Demo

Search and Retrieval Process

Cont…

video

Conclusion

There is a need for CBIR.It may be sufficient that a retrieval system present similar images, similar in some user-defined sense.CBIR has overcome all the limitation of Text Based Image Retrieval by considering the contents or features of image. To make it easy to search/retrieve/filter/exchange content to maintain archive, and to edit multimedia content etc. CBIR technology has been used and also using in several applications such as fingerprint identification, biodiversity information systems, digital libraries, crime prevention, medicine, historical research.

References

Remco, C.V., Mirela, T., “Content based image retrieval systems: a survey”.http://www.mathworks.in/matlabcentralhttp://stackoverflow.com/questions/14768364/algorithms-used-for-content-based-image-retrieval-systemsContent Based Image Retrieval(CBIR) System Based on the Clustering and Genetic Algorithm

-Eng. Ahmed K. Mikhraq

That is all, folks…Thank you for your

patience!

That is all, folks…Thank you for your

patience!

Good Luck!