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Abstract

Most of the thresholding procedures involved setting of boundaries based on grey values or intensities of image pixels. In this paper, the thresholding is to be done based on color values in natural images. The color thresholding technique is being carried out based on the adaptation and slight modification of the grey level thresholding algorithm. Multilevel thresholding has been conducted to the RGB color information of the object extract it from the background and other objects. Different natural images have been used in the study of color information. The results showed that by using the selected threshold values, the image segmentation technique has been able to separate the object from the background.
I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
Published Online February 2012 in MECS (http://www.mecs-press.org/)
DOI: 10.5815/ijigsp.2012.01.04
Color Thresholding Method for Image
Segmentation of Natural Images
Nilima Kulkarni
New Horizon College of Engineering, Bangalore, India
e-mail: kulkarninilima@gmail.com
Abstract—Most of the thresholding procedures involved
setting of boundaries based on grey values or intensities of
image pixels. In this paper, the thresholding is to be done
based on color values in natural images. The color
thresholding technique is being carried out based on the
adaptation and slight modification of the grey level
thresholding algorithm. Multilevel thresholding has been
conducted to the RGB color information of the object
extract it from the background and other objects. Different
natural images have been used in the study of color
information. The results showed that by using the selected
threshold values, the image segmentation technique has
been able to separate the object from the background.
Index Terms—Color image segmentation, Color
thresholding, Multilevel thresholding, Natural images,
RGB color information.
I. INTRODUCTION
Segmentation process subdivides an image into its
constituent regions or objects. The level of subdivision
depends on the problem being solved, where the
segmentation should stop when the objects of interest in
an application have been isolated.
Image segmentation refers to partitioning of an
image into different regions that are homogeneous or
“similar” in some image characteristics. It is usually the
first task of any image analysis process module and thus,
subsequent tasks rely strongly on the quality of
segmentation [10]. Various techniques have been
proposed in the literature where color, edges, and texture
were used as properties for segmentation. Using these
properties, images can be analyzed for use in several
applications including video surveillance, image
retrieval, medical imaging analysis, and object
classification.
On the outset, segmentation algorithms were
implemented using grayscale information only [2]. The
advancement in color technology facilitated the
achievement of meaningful segmentation of images as
described in [3, 4]. The use of color information can
significantly improve discrimination and recognition
capability over gray-level methods.
However, early procedures consisted of clustering
pixels by utilizing only color similarity. Spatial
locations and correlations of pixels were not taken into
account yielding, fragmented regions throughout the
image. Statistical methods, such as Classical Bayes
decision theory, which are based on previous
observation, have also been quite popular [5, 6].
However, these methods depend on global a priori
knowledge about the image content and organization.
Until recently, very little work had used underlying
physical models of the color image formation process in
developing color difference metrics.
Because the human eyes have adjustability for the
brightness, which we can only identified dozens of
Gray-scale at any point of complex image, but can
identify thousands of colors. In many cases, only utilize
gray-Level information cannot extract the target from
background; we must by means of color information.
Accordingly, with the rapidly improvement of computer
processing capabilities, the color image processing is
being more and more concerned by people [25, 31]. The
color image segmentation is also widely used in many
multimedia applications, for example; in order to
effectively scan large numbers of images and video data
in digital libraries, they all need to be compiled
directory, sorting and storage, the color and texture are
two most important features of information retrieval
based on its content in the images and video. Therefore,
the color and texture segmentation often used for
indexing and management of data; another example of
multimedia applications is the dissemination of
information in the network [26]. Today, a large number
of multimedia data streams sent on the Internet,
However, due to the bandwidth limitations; we need to
compress the data, and therefore it calls for image and
video segmentation.
Human eyes can distinguish thousands of colors but
can only distinguish 20 kinds of grayscale, so we can
easily and accurately find the target from the color
images. However, it is difficult to find out from the
gray-scale image. The reason is that color can provide
more information than grayscale. The color for the
pattern recognition and machine vision is very useful
and necessary [27]. At present, specifically applied to
the color image segmentation approach is not so much
as for the gray-scale images, most of proposed color
image segmentation methods are the combination of the
existing grayscale image segmentation method on the
basis of different color space. Commonly used for color
image segmentation methods are histogram threshold,
feature space clustering, region-based approach, based
on edge detection methods, fuzzy methods, artificial
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
Color Thresholding Method for Image Segmentation of Natural Images 29
neural network approach, based on physical model
methods, etc.
Another challenging aspect of image segmentation
is the extraction of perceptually relevant information.
Since humans are the ultimate users of most CBIR
systems, it is important to obtain segmentations that can
be used to organize image contents semantically,
according to categories that are meaningful to humans.
This requires the extraction of low-level image features
that can be correlated with high-level image semantics.
However, rather than trying to obtain a complete and
detailed description of every object in the scene, it may
be sufficient to isolate certain regions of perceptual
significance (such as “sky,” “water,” “mountains,” etc.)
that can be used to correctly classify an image into a
given category, such as “natural,” “man-made,”
“outdoor,” etc. [28].
The different methods of image segmentation
algorithms for images
• Edge based image segmentation method
• Adaptive thresolding method
• Watershed method
• Region growing by active contour method
• Quadtree method
• Fuzzy c means clustering method
Edge based image segmentation method : In the first
step, the canny edge detector is used to process the two
parameter images and then the derived edges are added
to derive the final edge detection results. After that local
thresholding technique is applied. Adaptive thresholding
method: Thresholding is called adaptive thresholding
when different thresholds are used for different regions
in the image [29]. This may also be known as local or
dynamic thresholding. Consider a grayscale document
image in which g(x, y) € [0, 255] be the intensity of a
pixel at location (x, y). In local adaptive thresholding
techniques, the aim is to compute a threshold t(x, y) for
each pixel such that
Watershed segmentation method: In image
watershed segmentation, altitude is represented by the
gray level of the pixels. All pixels throughout the same
catchment basin are connected with the minimum
altitude region of the basin [1]. The watershed lines
divide individual catchment basins. The high gradient
regions correspond to watershed lines and low gradient
regions correspond to catchment basins. Region
Growing by active contour method: Region-based active
contour method is to find the image partition that
minimizes a criterion including both region-based and
boundary-based terms. Each region is described by a
function named "descriptor" of the region. First, for a
given application like detection of moving objects,
various descriptors can be easily tested inside the same
theoretical framework. Second, this framework can be
applied to other applications [30].
Quad tree method: The QT structure allows to divide an
image within a complete tree representation, including
neighboring information. This spatial information can be
further used by a merging strategy which joins the QT
leaves using color and edge information. Fuzzy c means
clustering: The purpose of clustering is to identify
natural groupings of data from a large data set to
produce a concise representation of a system's behavior.
Fuzzy c-means (fcm) is a data clustering technique in
which a dataset is grouped into n clusters with every
data point in the dataset belonging to every cluster to a
certain degree [34, 35].
By regarding the image segmentation as a problem
of partitioning pixels into different clusters according to
their color similarity and spatial relation, we propose our
color image segmentation method automatically.
Image segmentation algorithms generally are based
on one of the two basic properties of intensity values:
discontinuity and similarity. Thresholding is a method
of similarity category. It partitions an image into regions
that are similar according to a set of predefined criteria.
There are various thresholding techniques and it is also a
fundamental approach to segmentation that enjoys a
significant degree of popularity, especially in
applications where speed is an important factor [17].
In this paper, a method of grey thresholding
technique is reviewed, and it is then being adapted to
suit with color images.
The remainder of the paper is organized as follows.
In section , a review of the gray thresholding
technique is presented. The proposed algorithm of
multilevel thresholding for natural images described in
Section . After that, application of the proposed
algorithm is discussed in section , and we draw our
conclusion in the last section.
II. GRAY THRESHOLDING
Traditionally, one simple way to accomplish
thresholding is by defining a range of brightness value
in the original image, then selects the pixels within the
range as belonging to the foreground and rejects all of
other pixels to the background. Such an image is then
usually displayed as a binary or two-level image [18].
The general rule for grey level pixel thresholding is as
follows
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
30 Color Thresholding Method for Image Segmentation of Natural Images
where T is the threshold value, f(x, y) is the original
pixel value, and g(x, y) is the resulted pixel value after
thresholding has been done. Equation 1 specifies 0 and
1 as output values, which will give the result as a true
binary image. Equation 1 can be further visualized by
Figure 1 as mappings of input grey level to output grey
level [19].
There could be more than one thresholding value at a
time, which change Equation 1, to
Where T1 is the lower threshold value and T2 is the
upper threshold value. Figure 2 shows the visualization
of how thresholding with a pair of threshold is being
done [20].
For color images, more than one variable
characterizes each pixel in the image, which allows
multi spectral thresholding [21]. In color imaging, each
pixel is characterized by three RGB values. However,
with multi spectral or multilayer images such as RGB
model, it can be difficult to specify the selection criteria.
The logical extension of thresholding is simply to place
brightness thresholds on each image, for instance to
specify the range of red, blue and green intensities.
These multiple criteria are then usually combined
with an AND operation (i.e. the pixel is defined as part
of the foreground if its three RGB components all lie
within the selected range). This logically equivalent to
segmenting each image plane individually, creating
separate binary images and then combining them with a
Boolean AND operator afterward. This color
thresholding method is widely used in the image
segmentation [20, 22, 23and 24].
III. PROPOSED MULTILEVEL
THRESHOLDING FOR NATURAL IMAGES
A brief study on the color information of the natural
images was carried out in order to get the most suitable
values for selection range of the threshold. The study
was carried out on different types of natural images,
which comprise of normal images, low quality images,
compressed images. The color thresholding technique
was carried out based on the color information of the
object to extract it images from the background and
other objects. This technique specifies the range of RGB
intensities for thresholding. The objects that lie outside
the selection range will be rejected. Therefore, it is very
important to determine the selection range because if
this threshold cannot acquire a suitable value, the
thresholding algorithm will extract pixels other than the
expected object.
The properties of the RGB pixels are being studied
to extract the important features from the image, for
example, if we are interested in green areas (called as
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
Color Thresholding Method for Image Segmentation of Natural Images 31
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
Forest) then based on the color information, the color
thresholding algorithm should be able to extract the
pixels of green color and reject pixels of other objects.
If we are interested in blue areas (called as Sky) then
color thresholding algorithm should able to extract blue
color and reject pixels of other objects. Following are
the steps for proposed approach
From the data in the table, it can be seen that there
are various combination of values that can be used to
determine the best threshold for this type of image.
Since the determination of the best result can only be
done by human observation, various attempts have been
done so that the results can be compared to select the
best values for the thresholding algorithm.
Among the combinations that have been considered
were the ranges of minimum and maximum values for
each of the RGB components, the average values, as
well as the obvious difference between each of the RGB
components.
Considering the minimum and maximum values of
RGB components, Equations (3), (4), and (5) have been
formulated to get the thresholding values for green color
(i.e. forest). However, the original values have been
modified to cater up to 10% of difference.
Fig. 3 Steps for RGB Thresholding algorithm
In this paper, we are dealing with the natural images
and we are interested in green areas (called as Forest)
and blue areas (called as Sky). We calculated range for
RGB intensities for green and blue colors, and then we
apply thresholding algorithm.
In order to view the important properties of each
segment so that necessary features and accurate value of
threshold can be obtained from the result, the
information is being gathered in a table. In this table,
among the features that are noted are the maximum and
minimum values for each of the RGB components in
green area and blue area respectively. The maximum
and minimum values for each of the pixels are also
noted to extract important characteristic of the RGB
values that may be converted into threshold values.
Summary of the findings from the study can be
visualized in Table 1 and Table 2.
where g1(x,y) is the gray value of pixel and red(x, y),
green(x, y) and blue(x, y) are the pixel values for each
of the red, green and blue components respectively.
From Equations (3), (4), and (5), it can be seen that
the original equation that has been mentioned in
Equations (1) and (2) have been slightly modified to
adopt the method of grey level thresholding to color
thresholding. For the new equations, each RGB
component is being treated independently. Since there
are three components, the thresholding process is being
done to one component at a time, and they are then
combined into 1 rule using a Boolean AND operator.
Another modification that has been made is that, the
output value is not 0 or 1, but either 255 (white pixel) or
retaining the old value of the pixel. This means that if
the value of that particular pixel falls in the range of the
rule whereby the output value is original pixel color, this
indicates object we are interested in. However, if it is
not fall within that range the gray value of the pixel is
retained.
TABLE 1 : RGB INFORMATION IN GREEN AREA
MIN MA X
RED
0
173
GREEN
10
2
255
BLUE 0 173
TABLE 2 : RGB INFORMATION IN BLUE AREA Equations (6), (7), and (8) have been formulated to
get the thresholding values for blue (i.e. Sky) color.
MIN MA X
RED
0
90
BLUE
127
255
GREEN 127 255
32 Color Thresholding Method for Image Segmentation of Natural Images
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
(d) (e)
It is observed from the experiments that, threshold
value Tr =173, Tg =102 and Tb =173 gives best results
for segmentation of green area (i.e. forest) in natural
image. And threshold value Tr =90, Tg =127 and Tb
=127 gives best results for segmentation of blue area (i.e.
sky) in natural image.
(f)
In order to determine whether the thresholding
method that has been carried out is successful or not, it
relies solely on human intervention. Therefore, the
threshold value need to be varied until acceptable results
are achieved, based on the human observation. That is
why, in carrying the color thresholding procedure, it
may be necessary to do a few level of thresholding in
order to get the best results.
(g) (h)
The thresholding procedure must be done to the red,
green and blue components. The thresholding on the
three colors may be combined into one complete rule
using the Boolean AND operator or it may also be
separated into two or more rules.
If more than one rule is being created, then it is
considered to be done on a few level of thresholding. In
this paper, the process of thresholding is being done
automatically based on the predetermined threshold
value.
(i)
(j) (K)
(a) (b)
(l)
(c)
Fig. 4 figures (a), (d), (g) and (j) are the original images
Figures (b), (e), (h) and (k) are the result of green
color segmentation and Figures (c), (f), (i ) and (l)
are the result of blue color segmentation.
Color Thresholding Method for Image Segmentation of Natural Images 33
Copyright © 2012 MECS I.J. Image, Graphics and Signal Processing, 2012, 1, 28-34
Fig (a), (d) and (g) shows original image. If we are
interested in green color segments (or forest), then we
can apply the thresholding values as in equation (3),(4)
and (5). The results are shown in fig (b),(e) and (h). If
we are interested in blue color segments (or sky), then
we can apply the thresholding values as in equation
(6),(7) and (8). The results are shown in fig (c),(f) and
(i). The proposed algorithm is applied on many natural
images and experimental results shows that it provides
simple and effective color image segmentation.
IV. CONCLUSION
A technique of image segmentation by conducting a
thresholding method for natural images has been
presented. The segmentation allows the elimination of a
great amount of unwanted pixels, and retained only
those pixels in object we are interested in. The resulted
images satisfactorily showed that by using the selected
threshold values, the image segmentation method has
been able to segment out the green area (i.e. forest) and
blue area (i.e. sky) from natural images.
The meaningful experiment results of color image
segmentation hold favorable consistency in terms of
human perception and satisfy the content-based image
retrieval and recognition process. There is one
disadvantage in proposed algorithm. Although using the
fixed threshold values can produce reasonably good
results, it may not generate the best results for all the
images. However this proposed thresholding algorithm
can be combine with other properties such as texture
into the algorithm to improve segmentation performance
is the point of our further research.
The performance of the proposed algorithms has been
demonstrated in the
domain
of
photographic images,
including
low resolution, degraded, and compressed
images.
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Nilima Kulkarni, female, is a master in engineering
(computer science and engineering) working as Asst. Professor
in New Horizon College of Engineering, Bangalore, and
Country India. Her research interests include digital image
processing, image segmentation and computer vision. Her
teaching interests include digital image processing, Object-
Oriented programming, and operating systems.
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