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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: rosymishra93@gmail.com
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
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 9
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
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 10
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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 11
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
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 12
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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 13
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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 14
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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 15
Fig. 12 describes clustering of Earthquake-2 image in MATLAB. Here we can generate image pixels by using HSV.
Fig. 13 describes clustering of Earthquake-3 image in MATLAB. Here we can generate image pixels by using HSV.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 16
Fig. 14 describes clustering of Earthquake-4 image in MATLAB. Here we can generate image pixels by using HSV.
Fig. 15 describes clustering of Earthquake-5 image in MATLAB. Here we can generate image pixels by using HSV.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 17
Fig. 16 describes clustering of Earthquake-6 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. 17 describes clustering of Earthquake-7 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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 18
Fig. 18 describes clustering of Earthquake-8 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. 19 describes clustering of Earthquake-9 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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 19
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%
10 Earthquake 2 70% 30%
11 Earthquake 3 60% 40%
12 Earthquake 4 54% 46%
13 Earthquake 5 20% 80%
14 Earthquake 6 10% 90%
15 Earthquake 7 50% 50%
16 Earthquake 8 80% 20%
17 Earthquake 9 70% 30%
18 Earthquake 10 10% 90%
19 Earthquake 11 60% 40%
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.
Int. J. Sci. Res. in Network Security and Communication Vol.7(4), Aug 2019, E-ISSN: 2321-3256
© 2019, IJSRNSC All Rights Reserved 20
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.
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