A NEW APPROACH FOR IMAGE SEGMENTATION USING
PILLAR K-MEANS ALGORITHM
ABSTRACT:
This project presents a new approach for image segmentation by applying Pillar-K means
algorithm. This segmentation process includes a new mechanism for clustering the elements of
high-resolution images in order to improve precision and reduce computation time. The system
applies K-means clustering to the image segmentation after optimized by Pillar Algorithm. The
Pillar algorithm considers the pillars’ placement which should be located as far as possible from
each other to withstand against the pressure distribution of a roof, as identical to the number of
centroids amongst the data distribution.
This algorithm is able to optimize the K-means clustering for image segmentation in
aspects of precision and computation time. It designates the initial cancroids’ positions by
calculating the accumulated distance metric between each data point and all previous cancroids,
and then selects data points which have the maximum distance as new initial cancroids. This
algorithm distributes all initial centroids according to the maximum accumulated distance metric.
This paper evaluates the proposed approach for image segmentation by comparing with K-means
and Gaussian Mixture Model algorithm and involving RGB, HSV, HSL and CIELAB color
spaces. The experimental results clarify the effectiveness of our approach to improve the
segmentation quality inspects of precision and computational time.
1. INTRODUCTION:
THE image segmentation is an effort to classify similar colors of image in the same
group. It clusters colors into several groups based on the closeness of color intensities inside an
image. The objective of the image segmentation is to extract the dominant colors. The image
segmentation is very important to simplify an information extraction from images, such as color,
texture, shape, and structure.
The applications of image segmentation are diversely in many fields such as image
compression, image retrieval, object detection, image enhancement, and medical image
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
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processing. Several approaches have been already introduced for image segmentation. The most
popular method for image segmentation is K-means algorithm. It is because of its ability to
cluster huge data points very quickly. Hierarchical clustering is also widely applied for image
segmentation. Many researchers used Gaussian Mixture Model with its variant Expectation
Maximization.
This project proposes a new approach for image segmentation that utilizes Pillar Algorithm to
optimize K-means clustering. The Pillar algorithm performs the pillars’ placement which should
be located as far as possible from each other to withstand against the pressure distribution of a
roof, as identical to the number of centroids amongst the data distribution. It designates the initial
centroids’ positions by calculating the accumulated distance metric between each data point and
all previous centroids, and then selects data points which have the maximum distance as new
initial centroids. The segmentation process by our approach includes a new mechanism for
clustering the elements of high-resolution images in order to improve precision and reduce
computation time.
2. OBJECTIVE
In this paper, we have presented a new approach for image segmentation using PillarKmeans algorithm. The system applies K-means clustering after optimized by Pillar Algorithm.
The Pillar algorithm considers the pillars’ placement which should be located as far as possible
from each other to withstand against the pressure distribution of a roof, as identical to the
number of centroids amongst the data distribution. This algorithm is able to optimize the Kmeans clustering for image segmentation in aspects of precision and computation time.
3. PROBLEM DEFINATION
Several approaches have been already introduced for image segmentation. The most
popular method for image segmentation is K-means algorithm. It is widely a used algorithm for
image segmentation because of its ability to cluster huge data points very quickly. Hierarchical
clustering is also widely applied for image segmentation. Many researches used Gaussian
Mixture Model with its variant Expectation Maximization.
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
2
4. PROPOSED METHOD
The image segmentation is important to unify contiguous colors in the color vector space
into representative colors. It can improve significantly performance of the information
extraction, such as color, shape, texture, and structure. This section describes our approach for
image segmentation using our proposed Pillar algorithm to optimize K-means clustering. The
image segmentation system pre-proceeds three steps: noise removal, color space transformation
and dataset normalization. First, the image is enhanced by applying adaptive noise removal
filtering. Then, our system provides a function to convert RGB of an image into HSL and
CIELAB color systems. Because of different ranges of data in HSL and CIELAB, we apply the
data normalization. Then, the system clusters the image for segmentation by applying K-means
clustering after optimized by Pillar algorithm. Fig. 1 shows the computational steps of our
approach for image segmentation.
5. SOFTWARE AND HARDWARE REQUIREMENTS

Operating system
:
Windows XP/7.

Coding Language
:
MATLAB

Tool
:
MATLAB R 2012
SYSTEM REQUIREMENTS:
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
3
HARDWARE REQUIREMENTS:

System

Hard Disk

Floppy Drive

Monitor

Mouse

Ram
:
Pentium IV 2.4 GHz.
:
:
1.44 Mb.
:
:
40 GB.
15 VGA Colour.
Logitech.
:
512 Mb.
APPLICATIONS:
1. Image compression
2. Image retrieval
3. Object detection
6.CONCLUSION
In this paper, we have presented a new approach for image segmentation using Pillar-K
means algorithm. The system applies K-means clustering after optimized by Pillar Algorithm.
The Pillar algorithm considers the pillars’ placement which should be located as far as possible
from each other to withstand against the pressure distribution of a roof, as identical to the
number of centroids amongst the data distribution. This algorithm is able to optimize the Kmeans clustering for image segmentation in aspects of precision and computation time. A series
of experiments involving four different color spaces with variance constraint and execution time
were conducted. The experimental results show that our proposed approach for image
segmentation using Pillar K means algorithm is able to improve the precision and enhance the
quality of image segmentation in all color spaces. It also performed the computational time as
fast as K-means and kept the high quality of results.
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
4
REFERENCES
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Segmentation
and
Pattern
Classification”,
Technical
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Artificial
Intelligence
Laboratory,1993.
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[3] A. Murli, L. D’Amore, V.D. Simone, “The Wiener Filter and Regularization Methods for
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[4] S. Ray, R.H. Turi, “Determination of number of clusters in K-means clustering and
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[8] Y. Gdalyahu, D. Weinshall, M. Wermen, “Self-Organizationin Vision: Stochastic clustering
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Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-mail: krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
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