Content-Based Image Retrieval

(CBIR)

By:

Shaik Raheem Pasha

What is CBIR ?

• Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users interests, has been an active and fast advancing research area since the 1990s.

• In our project we concentrated on histogram and Texture features to retrieve the images according to an example query image supplied by the user.

Block Diagram

Original Image

RGB

RGB HSV

Conversion

Histogram

Dist and Diff

Color Feature

Extract

RGB Gray

Conversion

2D-DWT

Transform

Higher

Component

Extraction

Texture

Feature Extract

Input Image

Color Feature Texture Feature

Pre-Processing

Feature Extract

Extracted Feature

Image Retrieval /

Similarity

Result Image

Histogram is a measure used to describe the image. In simple words it means the distribution of color brightness across the image. The brightness values range in [0..255].

What is Histogram

The histogram of a digital image with gray values 110 ,,, Lrrr is the discrete function

n

nrp kk )(

nk: Number of pixels with gray value rk n: total Number of pixels in the image

The function p(rk) represents the fraction of the total

number of pixels with gray value rk.

What is the histogram of a digital image?

Histogram provides a global description of the appearance of the

image.

If we consider the gray values in the image as realizations of a

random variable R, with some probability density, histogram provides

an approximation to this probability density. In other words,

)()Pr( kk rprR

0 1 1 2 4

2 1 0 0 2

5 2 0 0 4

1 1 2 4 1

The (intensity or brightness) histogram shows how many times a

particular grey level (intensity) appears in an image.

For example, 0 - black, 255 – white

0

1

2

3

4

5

6

7

0 1 2 3 4 5 6

Some Typical Histograms

The shape of a histogram provides useful information for

contrast enhancement.

Dark image

Bright image

Low contrast image

High contrast image

N

i

nmnm iimHiimHimHimHd1

),(),())(),((

N

i

nmnm iimHiimHimHimHd1

)),(),(min())(),((

Histogram Difference :

Histogram Intersection Distance :

Texture

Edge Density and Direction :

• Use an edge detector as the first step in texture analysis.

• The number of edge pixels in a fixed-size region tells us how

busy that region is.

• The directions of the edges also help characterize the texture

Two Edge-based Texture

Measures

1. Edge-ness per unit area

2. Edge magnitude and direction histograms

Fedgeness = |{ p / gradient_magnitude(p) threshold}| / N

where N is the size of the unit area

Fmagdir = ( Hmagnitude, Hdirection )

where these are the normalized histograms of gradient magnitudes

and gradient directions, respectively.

Local Binary Pattern Measure

100 101 103

40 50 80

50 60 90

• For each pixel p, create an 8-bit number b1 b2 b3 b4 b5 b6 b7 b8,

where bi = 0 if neighbor i has value less than or equal to p’s

value and 1 otherwise.

• Represent the texture in the image (or a region) by the

histogram of these numbers.

1 1 1 1 1 1 0 0

1 2 3

4

5

7 6

8

Original Image Frei-Chen Thresholded

Edge Image Edge Image

Example

Discrete Wavelet Transform

• The wavelet transform (WT) has gained widespread acceptance in

signal processing and image compression.

• Because of their inherent multi-resolution nature, wavelet-coding

schemes are especially suitable for applications where scalability

and tolerable degradation are important

• Recently the JPEG committee has released its new image coding

standard, JPEG-2000, which has been based upon DWT.

• Wavelet transform decomposes a signal into a set of basis functions.

• These basis functions are called wavelets

• Wavelets are obtained from a single prototype wavelet y(t) called mother wavelet by dilations and shifting:

• where a is the scaling parameter and b is the shifting parameter

)(1

)(,a

bt

atba

Discrete Wavelet Transform

Simulation and Results

• Here as per the flow of the system mentioned in

the block, the input image is applied for both the

feature extraction Methods.

• The Combined results of the methods is applied

to the database images and features are compared.

• Resultant Image will be displayed as matched and

in the otherwise condition the access is denied.

Design Screen Shot

Color Feature Extraction- Comparison

Texture Feature Extraction- Comparison

Conclusion and Future

Color information on some of information by image makes

usefulness but, as weakness of color information is that can search

the similar color range, different image. In present method image DB

retrieval by Image information, which combines the info about

Texture along with Color, in order to overcome the weakness of

color.

In Future In order to increase accuracy to more extent, test are

been applied for the Surf Algorithm to match with point wise

determination, in combination with color and texture.

THANK YOU