1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
407
POLLINATION BASED OPTIMIZATION FOR COLOR IMAGE
SEGMENTATION
Gaganpreet Kaur1
, Dr. Dheerendra Singh2
1
Assistant Professor, Department of CSE, Sri Guru Granth Sahib World University,
Fatehgarh Sahib, India
2
Professor & Head, Department of Computer Science & Engineering, SUSCET,
Tangori, Mohali, India
E-mail: 1
gagan1492003@yahoo.com, 2
professordsingh@gmail.com
ABSTRACT
Color image segmentation is a process of partitioning an image into disjoint regions, i.e. into subsets of
connected pixels which share similar color properties. Region extraction in color images is a difficult
process. I have proposed a new optimization method Pollination Based Optimization (PBO) to select best
optimal clusters in color images. The methodology consisted of four steps: color space conversion,
generation of candidate color cluster centers using Fuzzy K Means, pollination based optimization
method to select optimum color cluster centers, image segmentation. Pollination in flowers is used for
selecting optimal clusters in colored image. The optimization method worked well on images used. The
total elapsed time used to compute segmentation also reduced considerably.
Keywords: Segmentation, Clustering, Pollination Based Optimization (PBO)
INTRODUCTION
Segmentation involves partitioning an image into a set of homogeneous and meaningful regions,
such that the pixels in each partitioned region possess an identical set of properties [1]. Image
segmentation is one of the most challenging tasks in image processing and is a very important pre-
processing step in the problems in the area of image analysis, computer vision, and pattern recognition
[2]. In many applications, the quality of final object classification and scene interpretation depends largely
on the quality of the segmented output [3]. In segmentation, an image is partitioned into different non-
overlapping homogeneous regions, where the homogeneity of a region may be composed based on
different criteria such as gray level, color or texture. [1]. Image segmentation is a complex and hard task
in color images, but also it is the one of the important and curial problems in color image analysis.
Because high performing segmentation algorithms lead effective image recognition and retrieval systems
[4]. In this paper, PBO approach is used for color image segmentation by using clustering is used. The
paper is organized as follows. In section 2, pollination based Optimization (PBO) is discussed. In section
3, the design and implementation of PBO is discussed. Experimental results on images are presented in
section 4. Finally, in Section 5, some conclusions and directions for future work are discussed.
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
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2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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A. Image Segmentation
The segmentation procedures analyze the colors of pixels in order to distinguish the different objects
which constitute the scene observed by a color sensor or camera. It is a process of partitioning an image
into disjoint regions, i.e. into subsets of connected pixels which share similar color properties [5].
Segmentation schemes can be divided into two main approaches with respect to the used predicate [2].
The first one assumes that adjacent regions representing different objects present local discontinuities of
colors at their boundaries. The second one assumes that a region is a subset of connected pixels which
share similar color properties. The methods associated with this assumption are called region construction
methods and look for subsets of connected pixels whose colors are homogeneous [5]. These techniques
can be categorized into two main classes, whether the distribution of the pixel colors is analyzed either in
the image plane or in the color space [5].
B. Region construction based on a color space analysis
The color of each pixel can be represented in a color space, it is also possible to analyze the distribution
of pixel colors rather than examining the image plane. In the (R,G,B) color space, a color point is defined
by the color component levels of the corresponding pixel, namely red (R), green (G) and blue (B). It is
generally assumed that homogeneous regions in the image plane give rise to clusters of color points in the
color space, each cluster corresponding to a class of pixels which share similar color properties [5]. The
classes of pixels are constructed by means of a cluster identification scheme which is performed either by
an analysis of the color histogram or by a cluster analysis procedure. When the classes are constructed,
the pixels are assigned to one of them by means of a decision rule and are mapped back to the original
image plane to produce the segmentation. The regions of the segmented image are composed of
connected pixels which are assigned to the same classes. When the distribution of color points is analyzed
in the color space, the procedures generally lead to a noisy segmentation with small regions scattered
through the image. Usually, a spatial-based post-processing is performed to reconstruct the actual regions
in the image [6, 7].
C. Clustering-based segmentation
Clustering is the process of identifying natural groupings or clusters, within multidimensional data,
based on some similarity measure (e.g. Euclidean distance) [8], [9]. Clustering algorithms are used in
many applications, such as data mining, compression, image segmentation, machine learning etc. A
cluster is usually identified by a cluster center (or centroid) [10]. Data clustering is a difficult problem as
the clusters in data may have different shapes and sizes [9]. Most clustering algorithms are based on two
popular techniques known as hierarchical and partitional clustering [9]. In hierarchical clustering, the
output is "a tree showing a sequence of clustering with each clustering being a partition of the data set”.
Partitional clustering aims to optimize cluster centers, as well as the number of clusters [11]. Most
clustering algorithms require the number of clusters to be specified in advance [10]. Finding the
"optimum" number of clusters in a data set is usually a challenge since it requires a priori knowledge,
and/or ground truth about the data, which is not always available. The problem of finding the optimum
number of clusters in a data set has been the subject of several research efforts [10], however, despite the
amount of research in this area, the outcome is still unsatisfactory.
II. POLLINATION BASED OPTIMIZATION
Optimization is a natural process embedded in the living beings [12].Pollination is a process of transfer
of pollen from male parts of flower called anther to the female part called stigma of a flower. Some
flowers will develop seeds as a result of self-pollination, when pollen and pistil are from the same plant,
often (but not always) from the same flower. Other plants require cross-pollination: pollen and pistil must
be from different plants. Plants benefit from pollinators because the movement of pollen allows them to

3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
409
reproduce by setting seeds. However, pollinators don't know or care that the plant benefits. They pollinate
to get nectar and/or pollen from flowers to meet their energy requirements and to produce offspring. In
the economy of nature, the pollinators provide an important service to flowering plants, while the plants
pay with food for the pollinators and their offspring. The floral display, fragrance and nectar lure
pollinators and leads to pollination. Some species of plants optimize their nectar, display and fragrance
producing resources. If pollination process is proceeding smoothly the plants spend average resources. If
pollination process is above normal the plants reduce expenditure on resources for producing nectar,
floral display and fragrance in the flowers. If the pollination success goes below normal, plants increase
the resource expenditure such that more floral display, fragrance and nectar to attract pollinator. As more
pollinators and their number of visits increase the pollination success rate increases [12].
I have used the model suggested by Thakar et al. [13]. The model suggests that the reproductive
success for every plant can be modelled by the following expression.
R= ሺ
஺ൈ஽
ሺ‫ן‬ା஺ൈ஽ሻ
ሻ ൅
ഀ
‫ן‬శಲൈವ
ሻൈேು
஺ುାேು
െ ‫ܥ‬ሺܰ ൅ ‫ܦ‬ሻ (1)
Where
ߙ =variable denoting average display at a given average nectar content. (a = optimum D = 1.2)
A = Average Investment in Nectar Content of a species (A= optimum N = 0.9. its range is 0.8 -
1.4)
D = individual investment in display (0 - 1.2 typical 1.2)
N = individual investment in nectar (0.8 - 1.5 typical 1.2 at A = 0.9)
P = parameter related to pollinators learning efficiency P = m x a+c here m and c are
Constants. (Range 0.1 – 25, typical value.2)
C =proportionality constant relating investment to reproductive cost (1)
A. PBO ALGORITHM
Initialize a=1.2, A=0.9, D=1.2, N41.9, P=2,
number of_plants = 6;
number of weeks =6;
number_of_seasons =20 (number of iterations)
pollination_weekly_goal = [0.10 0.25 0.50 0.75 0.90 1.00]
Randomly generate Investment Vector (IV)*
For season = 1 to number o[seasons (iterations)
For week = 1: number of weeks
For k = 1: number of_plants
Evaluate R using equation 1
Based upon R, update IV
Evaluate Error = Goal - R
Based upon error update N, D, A
End
Exit, if Error acceptable
End
End
III. DESIGN AND IMPLEMENTATION OF POLLINATION BASED OPTIMIZATION FOR
COLOR IMAGE SEGMENTATION
From the literature, it has been found that there is a need to develop a new optimization for selecting
optimum clusters in colored image for image segmentation. The proposed model focuses on following
steps:
a) New optimization method based on pollination in flowers to select best clusters.

4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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b) The total time used to compute segmentation is reduced.
This is practically implemented using MATLAB 7.11.0 environment.
A. System Level Design
The work is implemented on three images-
1. Hestain.png
2. Fabric.png
3. Lion.jpg.
Figure 1 shows the original test images used in the work.
B. Algorithm Level Design
To segment image into regions, our method operates some successive tasks step by step. First, segment
the image into clusters using fuzzy K means clustering method .Then, pollination based optimization
algorithm is run to select optimum cluster points over candidate cluster center points set. Finally, pixels
are classified according to their closest cluster center point, and image is segmented into homogeneous
regions.
The Image Segmentation using PBO algorithm can be described with the following algorithm.
Step1) Take an image and convert it into Lab image.
Step2) Segment the image into clusters using Fuzzy K Means. Step3) Select the optimum cluster points
call PBO algorithm discussed in 2.1. This loop can be terminated after a predefined number of
generations or after an acceptable problem solution has been found.
Step 4) Segmented image is obtained.
Fig.1 Original Test Images (a) “Hestain.png” (b) “Fabric.png” (c) “Lion.jpg”
IV. RESULTS
The implementation was done on original images of hestain.png, fabric.png and lion.jpg for color
image segmentation using PBO approach shown in Figure 1. Clusters of the objects obtained from the
original images are shown in Figure 2, Figure 4 and Figure 6. Finally segmented homogeneous regions of
the original image are shown in Figure 3, Figure 5 and Figure 7.The implementation was done in Matlab
7.11.0. The time elapsed by new algorithm to identify the clusters and further segment the regions of
interest is in seconds as shown in Table1.

5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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Fig. 2 (a) Objects in Clusters 1 (b) Objects in Clusters 2 (c) Objects in Clusters 3 for image 1 (a)
Fig. 3 (a) Segmented Red Colored Region obtained from Image 1(a) (b) Segmented Purple Colored
Region (c) Segmented Magenta Colored Region
Fig. 4 (a) Objects in Clusters 1 (b) Objects in Clusters 2 (c) Objects in Clusters 3 for image 1 (b)
Fig. 5 (a) Segmented Red Colored Region obtained from Image 1(b) (b) Segmented Purple Colored
Region (c) Segmented Magenta Colored Region

6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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Fig. 6 (a) Objects in Clusters 1 (b) Objects in Clusters 2 (c) Objects in Clusters 3 for image 1 (c)
Fig. 7 (a) Segmented Red Colored Region obtained from Image 1(c) (b) Segmented Purple Colored
Region (c) Segmented Magenta Colored Region (d) Segmented Green Colored Region (e) Segmented
Yellow Colored Region
TABLE I: TIME TAKEN BY PBO BASED COLOR IMAGE SEGMENTATION
Optimization Technique
used/ Time taken to
segment an image
( in seconds)
POLLINATION BASED
OPTIMIZATION
(PBO)
Hestain.png 1.34
Fabric.png 1.59
Lion.jpg 3.66

7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – 6367(Print),
ISSN 0976 – 6375(Online) Volume 3, Issue 2, July- September (2012), © IAEME
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V. CONCLUSION
This paper derives new optimization technique based on pollination of plants to segment the color
images. This new optimization algorithm has been implemented in Matlab for extraction the optimal
clusters from the colored images. The new algorithm worked accurately on the colored images and the
time required to segment the images into distinct homogeneous regions was considerably reduced as
compared to other optimization algorithms.
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