Cyclone and Earthquake Recognition and Estimation using HSV … · 2019-09-16 · Cyclone and Earthquake Recognition and Estimation using HSV Colour Segmentation and Clustering

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IJSRNSC

Volume-7, Issue-4, August 2019 Research Paper

Int. J. Sc. Res. in Network Security

and Communication

E-ISSN:2321-3256

© 2019, IJSRNSC All Rights Reserved 8

Cyclone and Earthquake Recognition and Estimation using HSV Colour

Segmentation and Clustering

Rosy Mishra1*

, Rajaram Meher2, Pratibha Bhoi

3, Amisha Mohanty

4

1CSE, Vikash Institute of Technology , Biju Patnaik University Of Technology, Baragrh, Odisha, India

2 Physics Hons, Vikash degree college, Sambalpur University, Bargarh,Odisha,India 3 Math Hons, Vikash degree college, Sambalpur University, Bargarh,Odisha,India

4Zoology Hons, Vikash degree college, Sambalpur University, Bargarh,Odisha,India

*Corresponding Author: [email protected]

Received: 19/Jul/2018, Accepted: 15/Aug/2018, Published: 31/Aug/2019

Abstract- Natural calamities are a threat to human civilization since ancient age. Advancement of technology leads to attract

many researchers to pre-prediction of natural disaster such as Earthquake and Cyclone. Even it is a great challenge to estimate

the post damage. This paper depicts the Cyclone prior prediction using novel HSV based color segmentation using

unsupervised classification approach using K-means clustering. For the better survival of people and also economic point of

view, it is most important to know the causes and consequences of natural calamities like earthquake and cyclone. This can be

achieved through this research work by determining various combination of k-means clustering algorithm and using

correspondence filters. The research not only provides information about applications of image processing techniques but it

also provides a quick, easy, effortless knowledge about the effect of cyclone and earthquake in a given area. Rapid advances in

k-means clustering have made it possible to obtain images of the atmosphere using different HSV technologies, make weather

prediction better.

Keywords- HSV (Hue Saturation Value), Histogram, K-means clustering

I. INTRODUCTION

Cyclone is basically a atmospheric wind and pressure

system, characterized by low pressure at its centre and by

strong circular wind motion. Its direction is found to be

anticlockwise in Northern Hemisphere and clockwise in

Southern Hemisphere. Earthquake refers to the shaking of

the surface of the earth, resulting from the sudden release of

energy in the lithosphere that creates seismic waves. They

may also bigger landslides and sometimes volcanic activity.

An analysis was done taking into account the properties of

HSV (Hue Saturation Value), color space emphasizing on

the Perception of variation in Hue, saturation and intensity

value of an image pixel. The segmentation of an image is

carried out just to split an image into some meaningful parts

for better analysis, so that a higher degree of observation of

the image pixel can be made like, the foreground object and

the background. Segmentation is essential for identification

of objects present in a query image and in database

images. Wang et al have used the LUV values of a group of

4×4 pixel along with other three features which are obtained

by the wavelet transform of L component for determining

the region of interest. In Netra system and Blobworld system

region based retrieval has been used. We segment color

images by using the features that are extracted by the HSV

space as a step in region based matching approach in CBIR

[1]. The HSV color space is completely different from RGB

color space. Since it separates out the intensity (Luminance)

from the color information (Chromaticity). In between two

chromaticity exes, a difference in Hue of the pixel is visually

more prominent as compared to that of saturation.

Thus the histogram has bins, which can accumulate

count of pixels with same color is found. It helps to

generate 3 histograms separately, one for each channel and

then link them into one. Smith and Chang have used a color

set approach for the extraction of spatially localized color

information. They used the HS coordinates to form a two

dimension histogram where each bin contains the

percentage of pixels in the image having corresponding H

and S colors for that bin [1,2,3,4,5,6]. A one-dimensional

histogram was generated from HSV space, where a smooth

transition of color is obtained in the feature vector. This

helps us to use a window-based smoothing of histograms so

as to match similar color between a query and each of the

data-base image.

The rest of the paper is organized as follows: The work

discussed In section 2 is related to feature generation using

the HSV color space and pixel grouping k-means clustering

mailto:[email protected]

Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256


algorithm. In section 3 the proposed method used in this

work is discussed. The experiment result and recognition

rate is discussed in section 4. In section 5 we draw the

conclusion.

II. IMAGE SEGMENTATION USING FEATURES

FROM THE HSV COLOR SPACE

Analysis of the HSV Color Space Saturation gives an idea

about depth of color and shows that human eye is less

sensitive to its variation as compare to variation in Hue or

Intensity. Thus we use saturation value of a pixel to

determine whether the Hue or the intensity is more suitable

for human vision and thus ignoring the actual value of

saturation. The saturation threshold determining this

transition, once again depends upon intensity. For low

intensities, even for a high saturation, a color is found close

to gray value and vice versa shown in equation 1.

(1)

Where (x, y) is enhanced gray level pixel Intensity of

image, ( ) is weighted mask and (x+I, y+j) gray level pixel

Intensity function in both spatial x and y coordinate. The

HSV color model mathematically represented as in equation

2.

H= {

(2)

Where is represented in equation (3) and The Saturation

can be mathematically represented in equation (4).

{ ( ) ( )

( ) ( )( )

} (3)

S = 1 -

( )[min(R, G, B)] (4)

The value or Brightness component is mathematically

represented [5,6] in equation 5 as

V=max(R, G, B) (5)

It has been found that for higher values of intensity a

saturation of 0.2 differentiate between Hue and intensity

dominance is seen. Assuming the maximum intensity value

to be 255, we need to use the following threshold function to

determine the effect pixel which is to be represented by its Hue or its dominant features.

(v)=1.0

(6)

Where, v=0, th(v)=1.0, which means that all the colors are

approximated as black whatever be the Hue or the

Saturation. With increase in the value of intensity, saturation

threshold that separates Hue dominance from intensity

dominance goes down.

A three dimensional representation of the HSV

color space shows a hexacone, where the central vertical

axis represents the intensity. Hue is actually defined as an

angle in the range of [0,2π] in relation to the red axis with

at

and again red at 2π. Saturation is the purity of the color

or brilliance and intensity. It is measured as radial distance

from the central axis with values between 0 to 1 i.e. from

the centre to outer surface. When S=0 , moving along the

intensity axis one has to move from black to white through

many shades of gray . But when it is taken for intensity and

Hue, change in the saturation from 0 to 1,the perceived

color changes from a shade of gray to the saturated form of

color. Thus by lowering the saturation and looking from a

different angle, any color in HSV space can be transformed

into a shade of gray. The intensity value determines the

particular gray shade to which the transformation covers.

When saturation is near zero, all pixels (even) with different

Hue look alike and by increasing the saturation towards 1,

they start getting separated and are visually perceived as

true colors. Thus, for low value of saturation, the

approximated color is gray and for higher intensity level its

approximated color is its Hue.

Fig. 1 describes HSL and HSV wrapping hexagons in to

circles (Wiki)

A. Features Generation using the HSV color space

The pixels with sub-threshold saturation have been

represented by their gray values where as the other pixels

can be represented by their Hues. The feature generation

which is being used by us helps making approximation of

color of each pixel in the form of thresholding. While the

features generated from the RGB color space being

approximated by considering a Hue having higher order

bits. Segmentation by this method helps better

identification of objects in an image. On the other hand

histogram maintains a uniform color transition that helps us

to do a window based smoothing during retrieval. A

principle way to generate a color histogram of an image is

to link together ‘N’ higher order bits for the red, green and

blue values of the RGB space. The result found can be

compared with those which are generated using RGB

colors. This phenomenon is shown in detail I the fig-4. Fig-

4 consists of a number of solid color with varying degree

of intensities. This shows the result using RGB color bits. It

is found that a few colors can not be recognized as they

can’t be separated from the background. Also we found that



the background of white and gray are considered equivalent

due to approximation values.

This makes the HSV-based features very useful in

doing segmentation algorithm like clustering on the

approximated pixels. A deep analysis of the virtual

properties of the HSV color space has been done and its

usefulness in content based image retrieval application is

found.

B. Pixel grouping k-means clustering algorithm:

This algorithm in data mining and it has a biggest

advantage for classification of objects in to different groups

of a large data set by partitioning of a data set in to subset

(cluster) represents by the variable K.

∑ ∑ ( ) ( )

Where there are k clusters is the

centroid or mean point of all the points [3]. This is a

type of clustering algorithm which is applied in different

subjects of research education such as, biology, zoology,

medicine, psychology, sociology, criminology, geology etc.

C. Histogram Image:

Histogram is an accurate or sure representation of the

distribution of numerical data. It was first introduced by Karl

Pearson. The histogram of an image normally refers to a

histogram of the pixel intensity values. It shows the number

of pixels in an image like, for an 8-bit grayscale image there

may be 256 different possible intensities. Thus the histogram

graphically displays 256 numbers showing the distribution

of pixels. The exact output from the operation depends upon

the implementation – it may be a simple picture of required

histogram in a suitable format or it may be a data file[2]. It is

very simple. First of all the image is scanned at once and a

running count of the number of pixels is done. Thus the

reliable data is then used to construct a suitable histogram.

Fig. 2 describes the Histogram Image of cyclone in MATLAB.

The scales of the histogram in X-axis are represented in terms of

gray level and Y-axis in terms of pixel count.

III. RELATED WORK

The K-means algorithm is the most important unsupervised

learning technique. Clustering is the technique in which we

organize the pixel according to some features. For solving

this algorithm , first we have to take k numbers of clusters.

Those k numbers of clusters are selected randomly.

Fig. 3 describes Architecture of Segmentation and Histogram

Generation in MATLAB

K-means is simple and most important for centroid

calculation algorithm. It generally partitions the input data

into k numbers of clusters according to mapping of the

image pixel to the RGB color space. The clustering based

method such as K-means algorithm convert the image

based on the HSV (Hue, Saturation, Value).

IV. RESULT AND ANALYSIS

The progress of Cyclone and Earthquake in each area can be

known through the cloud depth images exhibiting

characteristic patterns at various stages of evolution. The

pixel features are chosen either by selecting the hue or the

intensity as a dominant property based on its saturation

value of pixel.

The proposed segmentation technique is implemented in

the working platform MATLAB (version 2013a) and it is

evaluated using six earthquake images and six cyclone

images, which are collected from some websites. The

sample image is considered to get the respective output

after RGB color space conversion, color sharpening and K-

means clustering segmentation. This algorithm is used to

segment the image. There are two distinct approaches to

Content Based Image segmentation and histogram-

generation.



Retrieval, i.e. image segmentation and histogram generation application which are applied methods for feature extraction.

Fig. 4 describes clustering of cyclone-1 image in MATLAB. We can generate features by utilizing the properties of HSV .

Fig. 5 describes clustering of cyclone-2 image in MATLAB. Color space for clustering the pixels in to segmented regions.. It shows the HSV converted image of the same image using approximated pixels after saturation threshold and also form a K-means partition image



Fig. 6 describes clustering of cyclone-3 image in MATLAB. It shows the HSV converted image of the same image using approximated

pixels after saturation threshold and also form a K-means partition image.

Fig. 7 describes clustering of cyclone-4 image in MATLAB. The K-means partition image which is then used to form the histogram.



Fig. 8 describes clustering of cyclone-5 image in MATLAB. The HSV based approximation is helpful in determining the intensity and the

shade variation near the edges of different objects there by sharpening the boundaries and retaining the information of each pixels as it is.

Fig. 9 describes clustering of cyclone-6 image in MATLAB. It shows the HSV converted image of the same image using approximated

pixels after saturation threshold and also form a K-means partition image.



Fig. 10 describes clustering of Earthquake-1 image in MATLAB. It shows the HSV converted image of the same image of earthquack

using approximated pixels after saturation threshold and also form a K-means partition image.

Fig. 11 describes clustering of Earthquake-2 image in MATLAB.



Fig. 12 describes clustering of Earthquake-2 image in MATLAB. Here we can generate image pixels by using HSV.








Fig. 16 describes clustering of Earthquake-6 image in MATLAB. . It shows the HSV converted image of the same image of earthquack












Fig. 20 describes clustering of Earthquake-10 image in MATLAB. Here we can generate the image by utilizing the HSV.

Histogram has numerous uses. The most common use being

to decide the value of threshold to be used while converting

a grayscale image to a binary image by thresholding. If the

image is found suitable for thresholding, then the histogram

will be considered bimodal i.e. the pixel intensities will be

clustered around two separated values. And a suitable

threshold for separation of these two groups will be found

somewhere in-between the two peaks in the histogram. If

such a distribution is not followed then, the possibility of

producing a good segmentation will be bleak. The present

investigation derives eleven earthquake clusters and thus

depicts that k-means has the potential to exhibit the

unsupervised clustering for earthquake analysis.

Thus for each pixel, we choose either its Hue or intensity as

dominant feature based on its saturation. Then segmentation

of the image is done by grouping pixels with similar features

using the k-means clustering algorithm.

Table-1 describes recognition rate of cyclone images.

Fig

.No

Input

Image

Cluster-

1

Cluster-

2

Clustering

Rate

Error

Rate

3 Cyclone

1

80% 20%

96%

4%

4 Cyclone 2 10% 90%

5 Cyclone 3 70% 30%

6 Cyclone 4 45% 55%

7 Cyclone 5 60% 40%

8 Cyclone 6 85% 15%

As the Hue and the intensity values are seen to belong to

same number space, the two data sets are gathered

separately, so that the colors and the gray value pixels are

not considered in the same cluster. In this algorithm, it starts

with K=2 and consecutively increasing the number of

clusters until there is an improvement in error, which falls

below a threshold or a maximum numbers of is reached.

Table-2 describes recognition rate of Earthquake images.

Fig.

No

Input mage Cluster-1 Cluster-2 Clustering

Rate

Error

Rate

9 Earthquake 1 60% 40%

94%

6%











Then the feature is extracted from each image pixel. After

being extracted the pixel features are clustered using K-

means clustering algorithm, so that they can be grouped in

to region of similar color.

V. CONCLUSION

There by, we have concluded that, some important properties

and features of RGB and HSV color space, and have

developed a framework for image segmentation and

histogram generation using K-means clustering algorithm.



The performance of the proposed segmentation was analyzed

using defined set of normal areas and affected areas. Finally

approximate reasoning for calculating shape and position of

cyclone and earthquake are detected.

This type of image processing can be used to analyze the

satellite captured images for various natural disasters like

Tsunami, Earthquake, Cyclones etc and can be used to locate

the affected area. It can be used to analyze the surface of

terrestrial bodies like stars, planets and moons. At

microscopic level it can be used to study the structure of

microorganisms, cells etc. In other ways it can be used to

predict rainfall by analyzing the cloud dens ity at different

areas using this method. By comparing more images from the

same perspective, we can be able to differentiate the changes

happened during a period of time.

REFERENCES

[1] Ishita Dutta , Sreeparna Banerjee , Mallika De , ” An Algorithm for

Pre-Processing of Satellite Images of Cyclone Clouds “ ,

International Journal of Computer Applications (0975 – 8887)

Volume 78 – No.15, September 2013.

[2] Shamik Sural, Gang Qian and Sakti Pramanik , ”Segmentation And

Histogram Generation Using The Hsv Color Space For Image

Retrieval” , IEEE ICIP 2002.

[3] Barik R.C., Mishra R. (2016) “ Comparative Analogy on

Classification and Clustering of Genomic Signal by a Novel Factor

Analysis and F-Score Method”. In: Artificial Intelligence and

Evolutionary Computations in Engineering Systems. Advances in

Intelligent Systems and Computing, vol 394. Springer, New Delhi

[4] Rajnisha Verma, Sagar Singh Rathore, Abhishek Verma ,” MRI

Segmentation using K-Means Clustering in HSV Transform ”,

International Journal of Advanced Research in Computer

Engineering & Technology (IJARCET) Volume 4 Issue 10,

October 2015.

[5] Nisha Agrawal , Sanjukta Urma , Sonam Padhan , Ram Ch. Barik,

“ Indian Agro Based Pest Region Detection by clustering and

Pseudo- Color Image Processing” in International Journal of

Engineering Research & Technology (IJET) ISSN:2278-0181.

[6]. R. C. Barik, R. Pati and H. S. Behera,“ Robust signal processing

compression for clustering of speech waveform and image

spectrum”, IEEE International Conference on Communication and

Signal Processing, April 2-4, 2015, India.

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Cyclone and Earthquake Recognition and Estimation using HSV … · 2019-09-16 · Cyclone and Earthquake Recognition and Estimation using HSV Colour Segmentation and Clustering