MMFO: modified moth flame optimization algorithm for region based RGB color image segmentation

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 10, No. 1, February 2020, pp. 196~201
ISSN: 2088-8708, DOI: 10.11591/ijece.v10i1.pp196-201  196
Journal homepage: http://ijece.iaescore.com/index.php/IJECE
MMFO: modified moth flame optimization algorithm for region
based RGB color image segmentation
Varshali Jaiswal1
, Varsha Sharma2
, Sunita Varma3
1,2School of Information Technology, RGPV, Bhopal, India
3
Department of Information Technology, SGSITS, RGPV, Indore, India
Article Info ABSTRACT
Article history:
Received Mar 10, 2019
Revised Aug 16, 2019
Accepted Aug 29, 2019
Region-based color image segmentation is elementary steps in image
processing and computer vision. The region-based color image segmentation
has faced the problem of multidimensionality. The color image is considered
in five-dimensional problems, in which three dimensions in color (RGB) and
two dimensions in geometry (luminosity layer and chromaticity layer). In this
paper, L*a*b color space conversion has been used to reduce the one
dimension and geometrically it converts in the array hence the further one
dimension has been reduced. This paper introduced, an improved algorithm
modified moth flame optimization (MMFO) algorithm for RGB color image
segmentation which is based on bio-inspired techniques. The simulation
results of MMFO for region based color image segmentation are performed
better as compared to PSO and GA, in terms of computation times for all
the images. The experiment results of this method gives clear segments based
on the different color and the different number of clusters is used during
the segmentation process.
Keywords:
Clustering
Genetic approach
Image segmentation
Moth-flame optimization
Particle swarm optimization
Copyright © 2020 Institute of Advanced Engineering and Science.
All rights reserved.
Corresponding Author:
Varshali Jaiswal,
School of Information Technology, University Teaching Department,
Rajiv Gandhi Proudyogiki Vishwavidyalaya (State Technological University of Madhya Pradesh),
Airport Road, Gandhi Nagar, Bhopal-462033, India.
Email: varshalijaiswal@gmail.com
1. INTRODUCTION
The digital image segmentation [1] becomes an important field of research to enhance
the performance of the identifying object classes. Image segmentation is the digital image processing
techniques by which an original image is divided into the different region. Many researchers have used
different techniques for image segmentation like region growing, edge detection, threshold-based methods,
clustering, etc. Clustering [2] is a method that can group objects or patterns into a predefined number of
clusters so that object or patterns share their properties. The cluster can be hierarchical and partitioned.
Hierarchical cluster produce a nested series of partitions while partition cluster produces only one partition.
In computer vision, image processing problem [3] requires color image segmentation in order to identify
a target and cluster the image into segments according to color, motion, texture, etc. Real life applications of
color image segmentation in medical image processing are tumors detection from the brain image, surgery by
computer system and study of anatomical, etc.
There are many researchers working towards region-based color image segmentation problems.
The region-based image segmentation [4] is processed in which image are partition based on properties of
their region like color, text, luminosities, etc. Color image segmentation [1] is a key problem in image
processing and their applications are endless. There are several methods used to find the color image
segmentation such as mean shift algorithm [5] and particle swarm optimization etc. Velayudham et al [6]
implemented a method for detecting and classifying brain tumor in medical images. In their research they
firstly remove unwanted noise using dual-tree complex wavelet packets and empirical mode decomposition.

Int J Elec & Comp Eng ISSN: 2088-8708 
MMFO: modified moth flame optimization algorithm for region based RGB color … (Varshali Jaiswal)
197
Medical image is segmented using a K-means clustering technique and Cuckoo-neuro fuzzy
algorithm used for detection of brain tumor. Puranik [7] utilize a fuzzy system for color classification and
image segmentation. The highest fitness value is chosen as the best set of fuzzy rules for image segmentation.
Sankari [8] proposed a method which used Glowworm swarm optimization (GSO) and the expectation-
maximization (EM) based clustering methods for image segmentation. The proposed approach is compared
with the standard Gaussian mixture models (GMM)-EM by using Berkley's image data set. Rand index
measure and global consistency error (GCE) are used for measuring the performance algorithm. Ma et al [9]
developed novel approach region based color image segmentation for skin lesions detection in medical
image. Their approach is a combination of speed function, saturation and color information. The main
objective of using that algorithm is to cluster pixel into small segments and pixel in each segment possesses
similar characteristics. Yanhui [10] proposed a neutrosophic adaptive mean shift clustering method for color
image segmentation. Wang [11] proposed color image segmentation by the pixel classification method using
QEMs and TSVM classifier. Cheng [12] proposed a region-growing approach for color image segmentation
which is based on 3-D clustering and labeling. The features of the algorithm are that it takes color similarity
and spatial proximity as input. The [13] proposed the genetic algorithm and classical c-means clustering
algorithm (CMA) based color image segmentation. Li [14] proposed color image segmentation method based
on an evolutionary approach to the control system. This method determines the threshold values of HSV
range. The disadvantages of this method are that takes times for time-consuming process of segmenting
the boundary of the color images. However Belahbib [15] proposed a genetic algorithm based clustering
method for color image segmentation. The output of the algorithm is a flexible string length with fitness
value. There are many approaches used for the color image segmentation like Rajinikanth [16] which used
for multi-level image segmentation. The performance measuring parameter is SSIM, PSNR and CPU time.
Amelio [17] investigates a graph-based approach for image segmentation. The results show that the method
is applicable for partitioning natural and human scenes. The authors conclude the genetic algorithm can be
a very efficient method compare to others.
The rest of the paper is organized as follows: research method is reviewed and describes the detailed
information about our proposed algorithm formed by the evolutionary approach in Section 2. Section 3 gives
the simulation and performance of the proposed algorithm and comparatively analysis of the proposed
algorithm with previous approaches in the literature. Conclusions and possible future research directions are
explored in section 4. The proposed method is tested on three different color RGB test images.
2. RESEARCH METHOD
In this section, the color image segmentation problem [18] is illustrated which is defined
mathematically by considering image I (i, j) to be segmented into K number of cluster. Where (i, j) is
represented pixel location. A grouping of the pixel having same color components is termed as color
clustering [19]. The color component of each pixel in image is represented by a vector. In RGB image
the vector is [R1, B1, G1] where R1, G1, B1 simultaneously represents red, green and blue vector
components. The pixel value represented by equation 1 which is given below as
0 <= R<= 255, 0 <= G<= 255, 0 <= B<= 255 (1)
In this paper, image is converted into L*a*b color model [15], where a* b* component represent
the color of pixel. This method convert 3D image into 2D image, which is shown in (2).
[RGB] → [A, b] (2)
Assuming that total N is number of pixel used to represent an image. Then a1,a2…….,aN and
b1,b2,…bN vector are used to represent their color component. The proposed method utilize clustering
algorithm for perform grouping of pixel in vector A and B which is shown in (3)
A⃗=[a1, a2,………..an], B⃗=[b1, b2,……,bn] (3)
The main objective of generating number of cluster in vector A and B is to find minimum distance
between them. The K is number of cluster that is farmed during the clustering process and each cluster is
represented by ac1, ac2,……ack and bc1,bc2,…….bck. The optimization function for the image segmentation is
defined by (4)
( . ) ( , )1
1
k
k
i j i jj
i
Min p d

  (4)

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 196 - 201
198
where ( . )i jp ={0, 1}, if ith
pixel is in the jth
cluster the ( . )i jp =1 otherwise ( . )i jp =0 and ( , )i jd is represented
by (5).
2 2
( , ) ( ) ( )i j i j i jd a a b b   
(5)
2.1. The proposed algorithm for region-based RGB color image segmentation
In this section, proposed algorithm is explained for the region based RGB color image
segmentation [14]. Region based RGB color images segmentation in which similar data points are grouped
together into clusters which is describe Figure 1. El Aziz [20] andn Mirjalili [21] recently proposed a swarm
optimization technique, which is inspired by behavior of moth and known as moth flame optimization (MFO)
algorithm. In this paper MFO algorithm applied for the region based RGB color image segmentation.
This transverse orientation seems useful only when the light is very far. When the light is too close, it directs
the spiral path towards the light which is shown in Figure 2. In the proposed MFO algorithm, assumptions are
taken to define as candidate solutions are moths and the problem’s variables are the position of moths in
the space. Therefore, the moths can fly in 1-D, 2-D, 3-D, or hyper dimensional space with respect to their
position vectors. By using the position of moths and flames MFO algorithm can be modeled. This is
represents the cluster center for a*b space of images. The fitness value given in equation 10 is optimized
within boundaries. Lower bouny [Lc1, Lc2,…. Lck], and Upper boun [Uc1, Uc2,......Uck] where Upper
boundary (Ui) <=255, Lower boundary (Li)=0, and i=1 to k. The dimensions of moth and flame are different
in space. Matrix is used to represent the position of all moth with corresponding fitness value. Similarly
the position of the all flame with corresponding fitness value which is describe in Figure 3.
Figure 1. Similar data points grouped together into
clusters
Figure 2. Logarithm spiral space around
the Flame and the Moth position with respect to t
The population-based MFO algorithm, in which matrix a, is used to describe for position of all Moth
and matrix b give fitness value of the corresponding Moth. Where n are the number of Moths and d number
of dimension variables.
M = [
m1,1 ⋯ m1,d
⋮ ⋮ ⋮
mn,1 ⋯ mn,d
] OM = [
OM1
OM2
⋮
OMn
] 𝐹 = [
𝐹1,1 ⋯ 𝐹1,𝑑
⋮ ⋮ ⋮
𝐹𝑛,1 ⋯ 𝐹𝑛,𝑑
] OF = [
OF1
OF2
⋮
OFn
]
(a) (b) (c) (d)
Figure 3. Matrixes represent Moth and Flame with corresponding fitness value
The result of fitness function for each Moth is another key component in the proposed algorithm.
The matrix c used to represent potation of all Flames and matrix d give fitness value of the corresponding
Flame. Matrixes in Figure 3 are used to simulate spiral flying path of Moths. Different parameters used by
MFO algorithms are iterations number, number of the predefined clusters and search agents number.
The proposed algorithm compared with GA and PSO algorithm. The GA [17, 22] in which first read RGB
color image, then for every cluster calculate L*a*b* factor for each color.

199
The Genetic-based clustering algorithm cluster the different color by draw graph of the 'a*'and 'b*'
values of pixels that were segmented into different colors. PSO [23] algorithm work based on the behavior of
birds to solve the optimization problems. In this algorithm, the particles can more quickly converge to
the optimal solution because the global best particle to provide information to other particles and all
the update process is to follow the current optimal solution [24, 25]. PSO works as fallows first initialize each
particle then for each particle calculate the fitness value and personal best (pBest).
2.2. Simulation and performance evaluation
In this section, we simulate the proposed algorithm. The aim of this experiment is to improve
the fitness value, segment colour image and plot the graph between a*b* colour labels and decreasing
computation time.
2.2.1. Simulation parameters
In the process of color segmentation, firstly read RGB image then converted into L*a*b space.
This process reduces one of the dimensions which represent the color information. The a*b space is
representing the color component of image and a*b* component are converted into on array. These reduce
the two dimensional geometric information into one dimension. This a*b* space information is then clustered
using proposed algorithm and each cluster is processed to find the color segment of image. A colour image
segmentation method is simulated using Genetic Approach, Particle Swarm Optimization and Moth-flame
optimization algorithm for clustering the images. The results are calculated by Matlab R2013a on an Intel(R)
Core(TM) i5-6500 CPU @3.20 GHz, 4.00 RAM running window 10.
The proposed method is tested on colour RGB test images from
https://homepages.cae.wisc.edu/~ece533/images/ (512*512 pixels). Using MATLAB tools understand
the colour image segmentation by proposed algorithm and tested on different test images from the database.
The typical results for the different image of the colours are shown in Figures 4 and Table 1 represents
simulation parameter used by the proposed algorithm. The fitness value of GA, PSO and MFO based
clustering algorithm are used to minimize the sum of distances between cluster centre and its members for
each cluster.
2.2.2. Performance metrics
In this section calculated performance of proposed algorithm and compared with other evolutionary
approach. All algorithm used in this paper have the same stopping conditions. In proposed algorithm fitness
value and computation time are used to evaluate the performance of RGB image segmentation. The fitness
value is showing the color difference in an image which is based on L*a*b* color luminous. The proposed
algorithm is using the concepts of Euclidean distance and L*a*b* color luminous [1]. The color
difference between two colors, (L1*, a1*, b1* and L2* a2* b2*) is calculate which is describe in (6) and
the performance of proposed algorithm are tabulated in Table 2.
Table 1. Simulation parameter for
different Images
Table 2. The best fitness value obtained from
the different algorithm
Image k iteration MFO PSO GA
Fitness value Fitness value Fitness value
Image1
5
1 223800.6438 173704.5129 144855.9356
50 113745.1509 104368.1311 104296.7006
100 107486.7579 104270.4416 104271.8102
150 105991.2988 104270.2473 104274.4183
200 104270.6937 104270.2471 104272.1806
4
1 204536.5125 184593.3676 218473.9842
50 123813.8133 119923.7911 119921.1493
100 119955.7861 119919.2042 119919.2217
150 119919.6327 119919.1969 119919.2118
200 119919.0330 119919.1969 119919.2112
3
1 240628.9909 208617.5766 189116.8434
50 154692.4700 154173.2727 154173.0245
100 154176.6397 154172.0967 154172.1260
150 154172.1028 154172.0962 154172.1231
200 154172.0962 154172.0962 154172.1215
2
1 271475.3318 246706.8284 263679.3727
50 236151.1116 234626.4496 234626.2914
100 234626.2796 234626.2687 234626.2687
150 234626.2687 234626.2687 234626.2687
200 234626.2687 234626.2687 234626.2687
S.No. Parameter Description
1 Parameter
for
algorithm
Image Size 512*512
Pixel
2 Image type PNG Image
3 K-Factor 2,3,4,5
4 Maximum no. of
Iteration
200
5 GA Crossover
Percentage 0.8
6 Mutation Rate 0.02
7 Mutation
Percentage 0.3
8 PSO Population Size
(Swarm Size) 50
9 Inertia Weight
Damping Ratio 0.99
10 Constriction
Coefficients 2.05
11 MFO Population Size
(Moth Size) 50
12
Population Size
(Moth Size) 50

 ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 10, No. 1, February 2020 : 196 - 201
200
2 2 2 2
1 1 2 2
1
( , ) ( , ) ( ) ( ) ( ) ( )
n
n n i i
i
d x y d y x y x y x y x y x

          (6)
3. RESULTS AND ANALYSIS
The performance comparison of proposed algorithm with other bio-inspired algorithm is illustrated
in Table 2 and Table 3. Table 2 shows that the MFO algorithm always started with the highest inter-class
cluster distances and Figure 5 indicate that all the algorithms performed nearly equally whenever K=4 for all
the images. Similarly the Figure 6 indicate that all the algorithms performed approximately in the same way
whenever K=5. However, the MFO algorithm gives highest fitness value and the PSO algorithm comes at
the second rank. When value of cluster K is (4, 5), MFO algorithm is better than the PSO and GA algorithm
at a highest value of region based color image segmentation. Which indicates that the MFO algorithm is more
effective for region based RGB color image segmentation. They show that, the proposed algorithm MFO for
region based color image segmentation is the better than PSO and GA, in terms of computation times for all
the images.
Figure 4. The region based RGB color segmented images obtained by all algorithms when the “K=2” and
graph between objective function & No. of iteration
Table 3. Computation time of all algorithms (s)
Image K Values MFO PSO GA
Image 1 5 13.084s 17.539 s 29.460 s
4 12.857 s 29.471 s 25.356 s
3 10.642 s 17.116 s 25.760 s
2 8.777 s 14.011 s 21.077 s
Figure 5. The graph of image1 when K= 4 with
objective value and number of iterations
Figure 6. The graph of image1 when K= 5 with
objective value and number of iterations
4. CONCLUSION
The proposed method successfully developed and tested for RGB colour images datasets and
obtained up to 90% cases the colour clusters. The simulation results show that the MFO algorithm is better
than genetic approach, particle swarm optimization algorithms in terms of computation time and fitness value

201
of segmentation of an RGB colour image. MFO concludes as an improved algorithm in terms of efficiency
and speed. The algorithm tested on datasets (https://homepages.cae.wisc.edu/ece533/images) and shows their
results with computation time, fitness value. Colour image segmentation using MFO algorithm can directly
apply to medical image segmentation, machine-learning, content-based image retrieval system, sea object
detection, and deals with the surveillance image processing which is beneficial for military purposes and
several other surveillance purposes. The future work will concentrate on high-performance region based
colour image segmentation by using other bio-inspired and artificial intelligence algorithms.
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