42 POLLINATION BASED OPTIMIZATION FOR COLOR … BASED... · POLLINATION BASED OPTIMIZATION FOR...

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 Kaur

1, Dr. Dheerendra Singh

2

1Assistant Professor, Department of CSE, Sri Guru Granth Sahib World University,

Fatehgarh Sahib, India 2Professor & Head, Department of Computer Science & Engineering, SUSCET,

Tangori, Mohali, India

E-mail: [email protected],

[email protected]

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 &

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)

ISSN 0976 – 6375(Online)

Volume 3, Issue 2, July- September (2012), pp. 407-414

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408

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



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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= ��

��

��

�� (1)

Where

� =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.



410

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.



411

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



412

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



413

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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