Image Features and its Representation

Guided by: Prof. Swati Jain

Prepared by: Payal Shah 09bit027

Contents of Presentation

• Introduction-image features• Types of image features• Properties of image features• Applications of image features• Representation of image features• Edge detection and various techniques for its

representation• Color feature representation(Histogram)

INTRODUCTION

IMAGE FEATURES

What is Image features?

When the input data is too large to be processed and redundant(much data, but not much information) then the input data will be transformed into a reduced representation called set of features (also named features vector).

Transforming the input data into the set of features is called feature representation.

General features: Application independent features such as color, texture, and shape. They can be further divided into:

•Pixel- level features•Local features•Global features

Types Of Image Features

•There are var ious types of local features.Low- level•Edge detect ion•Corner detect ionCurvature•Edge d irect ion•changing intens i ty  Image mot ion•Motion detect ion

Local Features

•Repeatability

•Quantity

•Accuracy

•Efficiency

Properties of the Feature

Image features are used ini. Face detectionii. Face recognit ion in smart homeiii. Smile detectioniv. Handwrit ing & Signature recognit ionv. Digital fi ngerprint

Use of Image Features

•Once we extract the necessary features from a given image, we need to store them eff iciently.

•How we are going to store these features is cal led its Representation.

•A feature class can have more than one representation associated with it .

Representation

Edge points are pixels at or around which the intensity values undergo a sharp variation.

Edge Detection

There are various techniques using which we can store the information about edges.1.)Simple representation 2.)Chain codes3.)Slope representation 4.)Signatures

Edge Representation

The simplest representation of a contour is using an ordered l ist of i ts edge points.Advantages:

very useful for polygon shapes.Give exact image when converted into

images.Disadvantages:

Not very eff ective representation for subsequent image analysis.

Not useful for representing curves.

Simple Representation

It Specifies the direct ion of each edge point along the contour.

Two types:1.) 4-connectivi ty2.)8-connectivi ty

• Start at the first edge point and go clockwise around the contour.• The direct ion to the next edge point is speci fied using one of the four or eight quantized direct ions.

Chain-code Representation

4-connectivity 8-connectivity

Code- 0033333323… Code- 07666655….

Advantage: More efficient representation than simple

representation. We have to store only the co-ordinates of initial point.

Disadvantages: Not useful for representing curve shapes.

Useful for representation of edges having specific directions

• Slope representation It is used for arbitrary directions.(1) start at the first edge point and go clockwise around the contour.(2) estimate the slope and arc length.(3) plot the slope versus the arc- length.

Slope Representation

This can be useful for representing curve edges, but it is not efficient way.

Plot the distance from the centroid to the boundary as a function of angle.

Signatures

Useful for representing curves as well as polygons.

The color feature is one of the most widely used visual features in image retrieval.

Color features

Histograms

The histogram function is defined over all possible intensity levels.

For each intensity level, its value is equal to the number of the pixels with that intensity.

Histograms

• A color histogram H for a given image is defined as a vector H = {h[1], h[2], . . . h[i], . . . , h[N]}

i =a color in the color histogram, h[i] =number of pixels in color i in that image,N =number of colors in the adopted color model.

• In order to compare images of different sizes, color histograms should be normalized.

• The normalized color histogram H′ is defined for h′[i] = h[i]/XY

XY =The total number of pixels in an image

Example

1 8 4 3 4 1 1 1 7 88 8 3 3 12 2 1 5 21 1 8 5 2

1 2 3 4 5 6 7 8

Example

Graph of the histogram function

Original image

Histogram of color image

Original image

The RGB color space

Properties of image histograms

• Histograms clustered at the low end correspond to dark images.

• Histograms clustered at the high end correspond to bright images.

• Histograms with small spread correspond to low contrast images (i.e., mostly dark, mostly bright).

• Histograms with wide spread correspond to high contrast images.

Advantages:• Robustness• Implementation simplicity• Low storage requirements

Disadvantage:• Two images with similar color histograms can

possess different contents.

Links:http://140.115.156.251/vclab/teacher/2010DIP/11%20Representation%20and%20Description.pdf

http://www.ee.surrey.ac.uk/CVSSP/Publications/papers/Mikolajczyk-EB-2008.pdf

http://classes.soe.ucsc.edu/cmpe264/Fall06/Lec5.pdf

http://www.sztaki.hu/conferences/ADBIS/17-Valova.pdf

Book:Digital Image Processing by Rafael C. Gonzalez and R. E. Woods

References

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