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 Total Downloads : 14
 Authors : Shanta H Biradar
 Paper ID : IJERTCONV3IS27002
 Volume & Issue : NCRTS – 2015 (Volume 3 – Issue 27)
 Published (First Online): 30072018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Reboost Image Segmentation using Genetic Algorithm
Shanta H Biradar
Information Science and Engineering Department
Sir M visvesvaraya Institute Of Technology Bangalore562157.
Abstract: This paper present a Improved Algorithm for Image Segmentation System for a RGB colour image, and presents a proposed efficient colour image segmentation algorithm based on evolutionary approach i.e. improved Genetic algorithm. The proposed technique, without any predefined parameters determines the optimum number of clusters for colour images. The optimal number of clusters is obtained by using maximum fitness value of population selection. The advantage of this method lies in the fact that no prior knowledge related to number of clusters is required to segment the color image. Proposed algorithm strongly supports the better quality of segmentation. Experiments on standard images have given the satisfactory and comparable results with other techniques.
General Terms: Digital Image Processing, Algorithm, Image Segmentation,
Genetic Algorithm.
Keywords: Color image segmentation, Genetic algorithm, Clustering.
1. INTRODUCTION

Image Segmentation
The goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects, or natural parts of objects. Some works have applied genetic algorithms (GA) to image processing [34] and to segmentation particularly [33, 32, 30, 31]. Indeed, GA is particularly efficient when the search space is really important and when the criterion to optimize is numerically complicated which is always the case in image processing. The main advantages of using GA for segmentation lie in their ability to determine the optimal number of regions of a segmentation result or to choose some features such as the size of the analysis window or some heuristic thresholds. In this paper we deal with various approaches for color image segmentation using GA along with many image segmentation techniques. We have tried to describe general segmentation techniques in this paper. In next section, we illustrate Mestimators algorithm (genetic algorithm), followed by comparison of various approaches led by researchers. Finally we end our work by giving the conclusion and perspectives.
Image
Feature Extraction
Object
Segmentation
Process
Recognition
Process
Process
Image
Feature Extraction
Object
Segmentation
Process
Recognition
Process
Process
Genetic Object
Algorithm Learning Model
Segmentatio for for new Acquisition
n Parameter Parameter Object and
Set Adaptation Feature Refinement
Fig.1: Flow Chart of Image Processing System

Genetic Algorithm
Genetic algorithms are based on natural selection discovered by Charles Darwin [40]. They employ natural selection of fittest individuals as optimization problem solver. Optimization is performed through natural exchange of genetic material between parents. Offsprings are formed from parent genes. Fitness of offsprings is evaluated. The fittest individuals are allowed to breed only. In computer world, genetic material is replaced by strings of bits and natural selection replaced by fitness function. Matting of parents is represented by crossover and mutation operations.
A simple GA (Figure 1) consists of five steps [29]

Start with a randomly generated population of N chromosomes, where N is the size of population, l length of chromosome x.

Calculate the fitness value of function (x) of each chromosome x in the population.

Repeat until N offsprings are created:

Probabilistically select a pair of chromosomes from current population using value of fitness function.

Produce an offspring yi using crossover and mutation operators, where i = 1, 2, , N.


Replace current population with newly created one.

Go to step 2.
Begin Optimization
Generate Random Population
Calculate Fitness (x)
Form an Offspring yi
Form an Offspring yi
Select Pair of Chromosome
N
Done
?
Y
P. Scheunders [37] proposed a genetic cmeans algorithm which is an improvement to cmeans clustering algorithm combining it with genetic algorithm. It is shown that this algorithm is less sensitive to the initial conditions than CMA. Furthermore GCMA is compared to several classical color image quantization algorithms, and its performance is shown to outperform the others, an effect which affects the visual image quality.
The GAbased GAHSI segmentation scheme [23] is a novel and simple approach to robustly segment an outdoor field image into plant and background regions under variable lighting conditions. The GAHSI obtained an equivalent segmentation performance to that obtained by applying cluster analysis to images acquired under specific lighting conditions. To further improve segmentation robustness, different imaging devices and color transformations as well as GA coding and operators need to be investigated in future research.
Li Zhuo et al [38] presented a mew GA based wrapper feature selection method GASVM for hyperspectral data. The results showed that the GASVM method could significantly reduce the computation cost while improving the classification accuracy.
N END Replace Old
? Population
Y
End Optimization
Fig.2: Flow Chart of Genetic Algorithm

LITERATURE REVIEW
Segmentation of a colour image composed of different kinds of texture regions can be a hard problem, namely to compute for an exact texture fields and a decision of the optimum number of segmentation areas in an image when it contains similar and/or unstationary texture fields. In this work, many researchers have proposed their algorithms for the same.
Vitorino Ramos, Fernando Muge [22] proposed an improvement of the kmeans clustering algorithm. This algorithm uses minimum distance criteria as the fitness function. The minimisation is based on the different belonging combinations, of all points in the feature space. Naturally that, such task will be simply if the number of colours in one image to segment is low; however for high number of points in this 3D colour space (i.e., the different number of colours) this minimisation is hard to compute. The respective computer time for segmentation were 14.96, 12.76 and 37.02 minutes when string lengths were 124, 64 and 468 bits long.
ISODATA algorithm [28] proposed by Mohamad Awad, Kacem Chehdi, and Ahmad Nasri has some further refinements by splitting and merging of clusters. Clusters are merged if either the number of members (pixel) in a cluster is less than a certain threshold or if the centres of two clusters are closer than a certain threshold. Clusters are split into two different clusters if the cluster standard deviation exceeds a predefined value, and the number of members (pixels) is twice the threshold for the minimum number of members. 2) ISODATA is the only unsupervised classification method that is used with ERDAS IMAGINE.
The number of bands used for classification was reduced from
<>198 to 13, while the classification accuracy increased from 88.81% to 92.51%. The optimized values of the two SVM kernel parameters were 95.0297 and 0.2021, respectively, which were different from the default values as used in the ENVI software.

GENETIC ALGORITHM BASED CLUSTERING
The searching capability of GAs can be used for the purpose of appropriately clustering a set of unlabeled points in Ndimension into K clusters [1]. In our proposed scheme, the same idea can be applied on image data. We consider a colour image of size mxn and every pixel has Red, Green and Blue components. The basic steps of the GAclustering algorithm for clustering image data are as follows:

Encoding
Each chromosome represents a solution which is a sequence of K cluster centres. For an N dimensional space, each cluster centre is mapped to N consecutive genes in the chromosome. For image datasets each gene is an integer representing an intensity value of the three components Red, Green and Blue.

Population initialization
Population is initialized in various rounds randomly and in each round the best chromosome survives for the next round processing.

Fitness computation
The fitness computation is accomplished in two steps. First, the pixel dataset is clustered according to the centres encoded in the chromosome under consideration, such that each intensity value
xi(r,g,b) of colour image combined with three component red, green and blue (24 bit), i = 1, 2, …, mxn is assigned to cluster
with centre zj(r,g,b), j = 1, 2, …, K,
If
The next step involves adjusting the values of the cluster centres encoded in the chromosome, replacing them by the mean points of
the respective clusters. The new centre Zi(r,g,b) for the cluster Ci is given by
Now the fitness metric is computed by calculating the sum of intra cluster spread, i.e. finding the sum of Euclidean distance between the pixels and their respective cluster, given by
The fitness evaluation of a chromosome is given by –
f = 1/M ..(1), thus our aim is to minimise the value of f.

Selection
Initially the fittest chromosome in every round of random population generation is moved to next generation, in the subsequent processing the fittest chromosome directly holds the 40% of the entire population and the rest of the population is hold by the chromosomes in the decreasing order of there fitness value.

Termination Criterion
We perform the population generation, fitness computation, crossover and selection for a predetermined number of generations, thus the algorithm is computed for the fixed number of generations and the best solution seen in the entire generation proceeds to final result.
Table 1. Analysis of Various Algorithms
Population size
Selection
Crossover
Mutation
Fitness criteria
Stopping
criteria
Coding
Characteris
tics
Future work
[22] Parallel 100
Fitness
One point
Substitutio
Distributio
Threshold
Integer
Using a
Automatic
Genetic Algorithm
proportio nal Model
crossover Random selection of the
n of a randomly generate integer
n function
technique based on entropy of distributed
(Chromoso me length
=5)
region based energy function, the active
Initialization
point
Crossover probabilit
Mutation probability
= 0.1
function
surface converges quickly
y=0.5
[23] GAHSI 48
Local
Single
Insertion
Weighted
Threshold
Binary
Use of
Adaptive
Algorithm
tourname nt selection over roulette wheel
point crossover
Crossover probabilit y=0.8
Mutation probability
= 0.03
average function
technique based on UTOPIA
parameter, if it failed for
different imaging devices and color transformati ons
adjustment of mutation rate
method
consecutive
5 times
[24] 6
Normaliz
Arithmeti
Non
Supervised
Stability of
Based on
Special
Implementati
Optimizatio
ed
c
uniform
evaluation
standard
genotype
focus on
on of priori
n Algorithm
geometri
crossover,
mutation,
function
deviation
evaluation
knowledge
c ranking
Crossover
Mutation
involving
of the
metrics
selection
probabilit
probability
classificatio
evaluation
method,
y=0.6
=0.05
n rate
criterion
Selection
probabili
ty = 0.08
[25, 26] 31
Expected
Unordere
Remove
Linear
Threshold
GA+SA+H
Distributed
Removal of
Hybrid Genetic Algorithm
value plan and Elitist plan are two selection strategies
d Subseque nce Exchange Crossover (USXX)
and Ordered Crossover
,
Crossover
and Reinsert, Mutation Probabilit y=0.03
Scaling function
Technique based on hybrid function
GAPSA
(Genetic Algorithm, Sequential Algorithm, Hybrid Genetic Algorithm with Parallel Simulated
environment with Remote Method Invocation concept
ambiguity in the population size. And more than
two meta heuristic algorithms may be interpreted to
Probabilit y=0.6
Annealing
improve the solution
space.
[27] 100
Proportio
Crossover
Mutation
average
Sub
Based on
GA
To reolve
Adaptive
nal
Probabilit
Probabilit
fitness
averaging
Schema
repeatedly
the problem
Genetic
selection
y, 0.5 < pc
y, 0.001 <
value f –
the fitness
Theorem
converges to
of getting
Algorithm
method
< 1.0
pm < 0.05
maximum fitness value (fmax f)
value
i.e. Binary Coding
almost same fitness values
stuck in the local optimum
when the population is
scattered.
[28] 90
Roulette
Cluster
Random
Objective
Stability of
Bit level
Usage of
Parallel
ISODATA
wheel
centre
substitutio
Function
fitness
coding
multicompo
cooperation
Algorithm
method
replaceme
n
values for
nent
with more
nt method
Mutation
20
features
segmentation
Crossover
probability
iterations
methods,
probabilit
= 0.1
such as FCM
y = 0.6
[36] 30
Roulette
Single
Substitutio
Cluster
Fixed no.
Integer
No prior
Work on
Dynamic
Wheel
point
n
validity
of
knowledge
generation of
GA
selection
crossover
Mutation
criteria
iterations
required
optimal no.
Based Clustering (GADCIS)
Crossover probabilit y=0.9
probability
=0.01
based on Gaussian distribution
of cluster centres
[37] Genetic No.
Roulette
One point
Insertion
Inverse of
Stability of
Binary or
Develops a
Data
cmeans
Of
wheel
crossover
of single
MSE
MSE
other
hybrid
clustering
Clustering (GCMA)
Clu ster S
selection
Crossover probabilit y=0.8
bit Mutation probability
=0.05
algorithm combining CMA and GA
methods
[38] GA 20
Stochasti
Single
Scale=1.0
Rank
Fixed no.
Binary
Classifies
Improvement
(based on SVM)
c uniform
point crossover
Crossover probabilit y=0.8
Shrink=1. 0
(fitness normalizati on)
of iterations
the given input data based on a set of training examples
in classification accuracy
scattered
[39] Elastic 200
3 level
One point
Substitutio
Normalized
Fitness
Integer
GA
Deviation of
Contour
contour
crossover
n
histogram
value
repeatedly
no. of
Method
function
Crossover probabilit y=0.6
Mutation probability
=0.0001
function
ranging around 500570
converges to almost same fitness values
variants


PROPOSED WORK
The proposed new segmentation algorithm can produce a better result according to the segments created by optimal number of dynamic clusters. We consider a colour image f of size m x n. The proposed algorithm is:

Repeat step 2 to 4 till fixed no. of generations.

Randomly generate the cluster set using the randperm function.

Each pixel of the image is associated with the cluster number using min distance function which uses Euclidean formula as criteria.

We calculate fitness value of the chromosome by adding the distances of each pixel.

The chromosome with maximum fitness value is considered as the solution.

RESULT
Testing of proposed algorithm with standard color images, it has given satisfactory results, a tabular comparison between different clustering techniques are presented below. Column 2 of table gives the optimal range of clusters as proposed by [12]. Assumptions for segmenting the images are as follows: 20 rounds are processed for generating the initial population each generating random cluster from range 2 to 8 in RGB space, from each round cluster with maximum fitness value is passed to next generation and at each next iteration fitness value is being compared with fitness value of next cluster set. Each next generation contains the maximum fitness value, cluster set and also the cluster numbers. An iteration to 20 generations has been done to create the final results.
Original Lena image Lena image by proposed Segmentation method
Original Mandril image Mandril image by proposed
Segmentation method
Fig.3: Original Image and Segmented Image of Lena & Mandril
Table 2. Comparison of different Image Segmentation Algorithm
Image
Optimal range
Proposed method
DCPSO
using v
SNOB
Mandril
5 to 10
5.32
6
39
Lena
5 to 10
4.47
6.85
31
Peppers
6 to 10
6.13
6.25
42
Jet
5 to 7
4.39
5.3
22

FUTURE WORK
The preceding sections provide an overview of the field of image segmentation, the review shows that many current algorithms are able to produce reasonable results on images of moderate complexity; several of these algorithms are efficient enough that they can be used as a preprocessing stage for higher level vision tasks such as recognition and tracking. The GAHSI algorithm
[23] has its own characteristics; still it has scope of improvement in adaptive adjustment of mutation rate. Elastic contour method [39] can be improved by automatic deviaton of no. of variants. In ISODATA algorithm [28], a parallel cooperation with various segmentation algorithms like FCM is required for further improvement. Still, there is some scope of improvement. Reviewing the existing algorithms, we conclude that absence of prior knowledge about the images contents, it is in general not possible to determine how many regions are required for a reasonable segmentation. This problem manifests in two forms, Undersegmentation, which occurs when parts of the image that actually correspond to different objects, or to an object and the background, are assigned to the same region; and over segmentation, which occurs when parts of the image corresponding to a single object are split apart. 
CONCLUSION
Genetic Algorithm has many advantages in obtaining the optimized solution. It was proved to be the most powerful optimization technique in a large space. Genetic algorithm allows performing robust search for finding the global optimum. The result of the optimization depends on the chromosome encoding scheme and involvement of genetic operators as well as on the fitness function. However the quality of image segmentation can be improved by selecting the parameters in an optimized way. The desire for improvement after the GA reached a near optimal stage, led the authors to put some efforts on implementation of prior knowledge applications of GAs in clustering and grouping problems are intensively described in [29]. In the present approach, grey level intensities of RGB image channels are considered as feature vectors, and the kmean clustering model (J.MacQueen, 1967) is then applied as a quantitative criterion (or GA objective fitness function), for guiding the evolutionary algorithm in his appropriate search.. In present scenario, various fast algorithms for speeding up the process of template matching are being implemented such as Mestimators for dealing with outliers. This fast algorithm ensures finding the global minimum of the robust template
matching problem in which a nondecreasing Mestimator serves as an error measure.

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