multilevel thresholding based on cuckoo search algorithm ...keywords: multilevel thresholding...
TRANSCRIPT
International Journal of Computer Engineering and Information Technology
VOL. 11, NO. 7, July 2019, 145–152
Available online at: www.ijceit.org
E-ISSN 2412-8856 (Online)
Multilevel Thresholding based on Cuckoo Search Algorithm using
Tsallis's Objective Function for Coastal Video Image Segmentation
I Gusti Ngurah Agung Pawana P1, I Made Oka Widyantara
2 and NMAED Wirastuti
3
1, 2, 3
Electrical Engineering Department, Engineering Faculty, Udayana University, Indonesia
3dewi.wirastuti @unud.ac.id
ABSTRACT Image segmentation could be a difficult surroundings is a due to
the presence of weakly correlated and ambiguous multiple
regions of interest. Many algorithms are developed to get
optimum threshold values for segmenting satellite images with
efficiency in their quality and blurred regions of image. In this
paper a novel multilevel thresholding algorithm using a Cuckoo
Search (CS) algorithm has been proposed for solving the coastal
video image segmentation problem. The optimum threshold
values are determined by the maximization of Tsaliis’s objective
function using CS algorithm. In this paper, the analysis of CS
algorithm performance is combined with Tsallis's objective
function. Based on evaluations of PSNR, FSIM and Convergence
characteristi CS, the Algorithm CS based on Tsallis objective
function evolved to be most promising and computationally
efficient for segmenting coastal video images achieve stable
global optimum thresholds. The experiments results encourages
related researches in computer vision, remote sensing and image
processing applications.
Keywords: Multilevel Thresholding Segmentation, CS
Algorithm, Coastal Video Image, Tsallis Method.
1. INTRODUCTION
Image segmentation is a basic research technology in
image processing. It can segments an image into groups is
based on several rules or models [4][9][10]. Image
segmentation is widely used in object detection, area
detection, and many other image processing applications
[26].
The coastal area is a transitional area between land and sea,
which is influenced by three influencing spells, namely
water, air, and land, so it requires a very specific
understanding. In partitioning objects in an image, the
thresholding technique uses a gray level value to define the
object boundary. The coastal area is a transitional area
between land and sea, which is influenced by three
influencing spells, namely water, air, and land, so it
requires a very specific understanding. In partitioning
objects in an image, the thresholding technique uses a gray
level value to define the object boundary.
Within the framework of coastal video image processing,
some thresholding-based segmentation techniques are
proposed in the literature. Liu et al. [19] detect coastline
change from satellite images based on onshore slope
estimation in a tidal flat. The approach [6] evaluation of
the coastline precision combines uniformity features and
the averaged image that represents a simple way of facing
textural characteristics. There are several non-monitoring
grouping methods, such as the Markov Random Fields [7]
Hierarchy and K-means [20], etc., which are exploited to
extract Coastal changes. Niedermeier et al. [23] published
approval and active contours for shoreline approvals
obtained from SAR images. Grouping interrelated pixels
between dry (dry sand) and wet (wet and air sand) using
the thresholding technique of bimodal histograms has been
supported by [29][22].
In the literature, most parametric and nonparametric bi-
level and multi-level setting procedures have been
proposed and applied mainly to gray scale images
[1][2][12][28]. Among them global threshold is considered
as the most preferred image segmentation technique
because of its simplicity, resilience, accuracy and
competence [31]. The global histogram based
segmentation technique can determine the threshold value
in multilevel thresholding. Most of them include image
thresholding technique, Otsu method that works based on
variance between classes [25] and the Kapur method
which works based on the entropy principle both prove to
be the best [16]. The Otsu and Kapur methods find the
optimal threshold that optimally divides the gray level
value of an image into several predetermined criteria. To
select the optimal threshold value, the Otsu method uses
class variants to maximize the gray histogram value, while
the Kapur method is used to maximize the histogram
entropy. However, the Otsu and Kapur methods are only
able to solve bi-level threshold problems. Both methods
will experience problems in computational time
146
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
complexity if used in multilevel thresholding problems.
Bhandari et al. Presented an evolutionary algorithm based
color image segmentation technique using Tsallis entropy,
where the researchers suggested the q parameter as a
tuning factor to determine the threshold value for
segmentation [3]. The qualitative and quantitative
experimental results show that the proposed method
selects the threshold value effectively and correctly. A
global nonparametric approach, the Otsu, Kapur, Tsai, and
Kittler methods are simpler and successful for bi-level
thresholding [28]. When the number of threshold levels
increases, the complexity of thresholding problems also
increases and traditional methods require more
computational time. In overcoming the computational
complexity of most global methods, bi-level procedures
and heuristic based multilevels have been proposed by
researchers for gray scale [1][2], RGB [32], multi-spectral
and hyperspectral images [13][14].
Optimization algorithms are developed with the aim of
solving complex problems in terms of time and resources.
Meta-heuristic algorithms such as cuckoo search
[1][33][37], bee colony [1], and firefly [31] Genetic
algorithms (GA) [17], ant colony optimization [38], honey
bee optimization [15], particle swam optimization (PSO)
[21], and bacterial foraging algorithm (BF) [30] used to
solve the m-level image thresholding problem. Most of the
methods discussed above are applied and validated in gray
scale image classes. Genetic algorithms are inspired by the
idea of evolution of natural selection which effectively
finds optimal thresholds. Research [8] has shown that
genetic algorithms have a tendency to meet optima locally
rather than global values that cause misclassification of
objects in segmented images. Whereas the PSO shows
some unsatisfactory problems such as the inability to find
global optimization values, low speed of convergence, and
so on. BF algorithm provides good performance based on
the quality of the solution and the speed of convergence
compared to other multilevel threshold methods. However,
the robustness and efficiency of the BF algorithm depends
on the size of the chemo taxi step, ie a large step size helps
bacteria to find optimal positions faster but does not ensure
global optimality. On the other hand, the small step size of
the chemo taxis guarantees that bacteria will find a global
optimum but require a large processing time.
In general, the parametric thresholding approach that is
computationally expensive, time consuming, and several
times its performance decreases depending on image
quality [18] [27]. Therefore, several studies have proposed
several methods for increasing the convergence rate of
optimization algorithms by keeping computing time low.
The multi thresholding harmony search algorithm (HSMA)
has been proposed by [24] as an effort to reduce
computational overhead in optimization algorithms for
image segmentation. Research [35] presents a new
approach to automatic shoreline detection based on video
images. Combine the original harmony search algorithm
(HSA) and the Cretaceous algorithm as an objective
function to get the optimal threshold value to improve
segmentation quality. HSMA takes a random sample in the
search space in the image histogram. These samples build
every harmony (candidate solution) in the context of the
HSA, while the quality is evaluated by considering the
objective function used by the Otsu or Cretaceous method.
Another approach is to implement multilevel segmentation
based on the Cuckoo Search (CS) algorithm proposed by
[33]. The CS algorithm requires fewer search parameters
compared to other optimization algorithms. Theoretical
analysis has shown that the update equation of the cuckoo
search algorithm satisfies the nature of global convergence.
So the convergence of CS algorithms to global optimal
solutions is guaranteed, while other algorithms converge
rapidly, but not to the best globally.
CS has been designed to solve continuous problems, so
that CS adjustments are needed in the construction of
coastline detection research. In this study focuses on new
approaches to image segmentation. Combining the CS
algorithm method is based on its performance to produce a
stable random value and optimize Tsallis's objective
function by finding the threshold value in the multilevel
segmentation of coastal video images. This research is
organized as follows: Part 2 briefly describes various
thresholding techniques for image segmentation. Section 3
provides an overview of nature-inspired algorithms, which
were used in this study. Section 4 evaluates the results of
the performance measurements of the Cuckoo Search
algorithm which are validated by a series of numbers and
tables. Finally Section 5 presents the conclusions of this
study.
2. THRESHOLDING TECHNIQUES FOR
IMAGE SEGMENTATION
The optimization problem is to find variable values that
optimize objective / fitness functions. This paper, the
objective function for the CS algorithm at the multilevel
threshold is designed based on Tsallis entropy.
2.1 Multilevel Thresholding
Graylevel contained in the image is very thin, the simple
thresholding method is not able to handle this type of
image. So that a method such as multilevel thresholding is
needed to divide the gray level levels into the appropriate
sub-regions. Multilevel thresholding is a process that cuts
graylevel in an image into several clear regions. This type
of thresholding technique requires more than one threshold
for input image and cutting image into certain regions. It
can be mathematically formulated as:
𝑇0 = 𝐼 𝑥,𝑦 ∈ 𝑋|0 ≤ 𝐼 𝑥, 𝑦 ≤ 𝑡1 − 1 ;
147
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
𝑇0 = 𝐼 𝑥,𝑦 ∈ 𝑋|0 ≤ 𝐼 𝑥, 𝑦 ≤ 𝑡1 − 1 ; 𝑇1 = 𝐼 𝑥,𝑦 ∈ 𝑋|𝑡1 ≤ 𝐼 𝑥,𝑦 ≤ 𝑡2 − 1 ; 𝑇𝑖 = 𝐼 𝑥,𝑦 ∈ 𝑋|𝑡𝑖 ≤ 𝐼 𝑥,𝑦 ≤ 𝑡𝑖+1 − 1 ; 𝑇𝑚 = 𝐼 𝑥,𝑦 ∈ 𝑋|𝑡𝑚 ≤ 𝐼 𝑥,𝑦 ≤ 𝑁 − 1 ; (1)
where X is the image used for processing, I(x, y) shows the
value of pixel intensity referred by the coordinate values
specified by x and y and ti = 1, 2 . . . m where m is the
total number of distinct threshold values.
2.2 Tsallis entropy
In image processing, the Tsallis entropy method can be
used for threshold search. This threshold search is
generally used to separate the background and foreground
parts of an image. The commonly used way is to change
the image first into a grayscale image and then compile a
histogram by calculating how many pixels of that image
have graylevel x. Each gray level x will then be tested to
determine the most optimal threshold. for each class, the
tsallis entropy equation can be formula given en Eq. (2) as
follows:
𝑠𝑞 =1− 𝑝𝑖
𝑞𝑘𝑖=1
𝑞 − 1 (2)
where pi ranges from 0 to 1 which denotes the probability
of the modeled system to be in state i. The parameter q
which gives the measure of non-extensivity of the system
under consideration is called as Tsallis parameter. So to
get tsallis entropy from the whole image, it can be written
as follows:
𝑆𝑞 𝑓𝑔𝑐 + 𝑏𝑔
𝑐 = 𝑆𝑞 𝑓𝑔𝑐 + 𝑆𝑞 𝑏𝑔
𝑐 + 1− 𝑞 . 𝑆𝑞 𝑓𝑔𝑐 .𝑆𝑞 𝑏𝑔
𝑐 where 𝑐 = 1, for gray− scale images (3)
where fg and bg shows the foreground and background of
an image. This method can be used for segmenting gray
scale and color images. Multilevel (m-level) image
thresholding by Tsallis entropy method [17][16] thus can
be formulated as:
𝑆𝑞𝑐0 𝑡 =
1− 𝑝𝑖𝑐
𝑝𝑖𝑐𝑡1−1
𝑖=0
𝑡1−1𝑖=0
𝑞 − 1; 𝑆𝑞
𝑐1 (𝑡) =
1− 𝑝𝑖𝑐
𝑝𝑖𝑐𝑡2−1
𝑖=𝑡1
𝑡2−1𝑖=𝑡1
𝑞 − 1
𝑆𝑞𝑐𝑗 𝑡 =
1− 𝑝𝑖𝑐
𝑝𝑖𝑐𝑡𝑗+1−1
𝑖=𝑡𝑗
𝑡1−1𝑖=𝑡𝑗
𝑞 − 1; 𝑆𝑞
𝑐𝑚 (𝑡) =
1− 𝑝𝑖𝑐
𝑝𝑖𝑐𝑡−1
𝑖=𝑡𝑚
𝑁−1𝑖=𝑡𝑚
𝑞 − 1 (4)
yielding the optimum threshold values as:
𝑡0 , 𝑡1 ,… . 𝑡𝑚 = arg𝑚𝑎𝑥 𝑆𝑞𝑐0 𝑡 + 𝑆𝑞
𝑐1 𝑡 … . 𝑆𝑞𝑐𝑚 𝑡
+1 1− 𝑞 . 𝑆𝑞𝑐0 𝑡 . 𝑆𝑞
𝑐1 𝑡 .𝑆𝑞𝑐𝑚 (𝑡)
subject to 𝑃𝑐0 + 𝑃𝑐1 − 1 < 𝑆𝑐0 < 1− 𝑃𝑐0 + 𝑃𝑐1 ;
𝑃𝑐1 + 𝑃𝑐2 − 1 < 𝑆𝑐1 < 1− 𝑃𝑐1 + 𝑃𝑐2 ; 𝑃𝑐(𝑚−1) + 𝑃𝑐𝑚 − 1 < 𝑆𝑐(𝑚−1) < 1− 𝑃𝑐(𝑚−1) + 𝑃𝑐𝑚 (5)
𝑃𝑐0 ,𝑃𝑐1 ,…𝑃𝑐𝑚 can be formed from the probability
distribution of pixel values corresponding to the threshold
levels 𝑡0 ∗, 𝑡1 ∗, . . . 𝑡𝑚 ∗ given by:
𝑃𝑐0 = 𝑝𝑖𝑐
𝑡1−1
𝑖=0
; 𝑃𝑐1 = 𝑝𝑖𝑐
𝑡2−1
𝑖=𝑡1
; …𝑃𝑐𝑚 = 𝑝𝑖𝑐
𝐿−1
𝑖=𝑡𝑚
; (6)
2.3 Cuckoo Search (CS) algorithm
Cuckoo Search found by [28] is a meta-heuristic algorithm
adopted from the behavior of cuckoo birds in breeding.
Cuckoo Search is categorized as a natural-inspired strategy
whose concept is inspired by natural events. The
philosophy and process contained in Cuckoo Search are
inspired by the cuckoo habit in its breeding process. Due
to the good individual selection to be brought to the next
generation, Cuckoo Search can also be categorized as
Evolutionary Computation (EC). In the optimization
process, for simplification use several lines:
1. One egg will be laid at a time by each cuckoo in any
nest chosen randomly.
2. Nest which have the best quality eggs are carried
over to the forthcoming generation.
3. The probability of host species discovering cuckoo’s
egg lies within the probability range pa [0,1] and
the total number if nests if fixed.
min max min
,0,1
i j j j jx x rand x x (7)
When the generation of new solutions x(t+1)
for a cuckoo i,
L´evy flight is shown as follows:
𝑥𝑖 𝑡 + 1 = 𝑥𝑖 𝑡 + 𝛼 ⊕ 𝐿𝑒𝑣𝑦(𝜆) (8)
where 𝛼 > 0 is the length of steps related to the scale of
the problem (can be used 𝛼 = 1 ) and ⊕ is the entrywise
multiplication. In this problem a long step is used,
mengikuti which follows the CS algorithm:
𝑠𝑡𝑒𝑝 =𝑢
|𝑣|1/𝛽, 0 < 𝛽 ≤ 2
𝑢~𝑁 0,𝜎𝑢 ,𝑣~𝑁 0,𝜎𝑣
𝜎𝑢 = 𝑟 1 + 𝛽 sin 𝜋𝛽/2
𝑟 1 + 𝛽 /2 𝛽2 𝛽/2 /2
1/𝛽
𝜎𝑣 = 1
(9)
where 𝛼 step size. Lévy Flights simulates randomly
following Lévy distribution as follows:
𝐿é𝑣𝑦 𝜆 = 𝑡−𝜆 ; 1 < 𝜆 ≤ 3 (10)
Nonlinear relationships of variance in retribution rates as
given in Equation (11) help in exploring search spaces that
are not known to be more efficient than models with linear
relationships.
𝜎2 𝑡 ~𝑡2−𝛽 ; 1 ≤ 𝛽 ≤ 2 (11)
The repetitive process continues until it reaches the global
optimum. The CS algorithm flow is given in Figure 1.
148
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
3. IMAGE SEGMENTATION USING
CUCKOO SEARCH
In segmenting coastal video images, video images are
partitioned into different classes and each class has
different segmentation quality and unclear images on the
object. To overcome variations in image quality and
blurred image areas, a multilevel threshold technique with
an optimization algorithm is used, so as to select the
optimal threshold value in image segmentation. Selecting
the optimal threshold until the threshold does not have a
change is important in segmentation. The purpose of this
work is to improve the quality and accuracy of image
segmentation using multilevel thresholding techniques
based on the CS algorithm. The developed segmentation
system consists of three main modules (Fig. 2): set
objective function, segmentation used CS, threshold
selection, image segmentation. The data parameters used
are as follows:
Table 1: Parameters used for CS
Parameters Value
Number of nest 25
Alien egg discovery rate pa 0.5
Bound of value for egg 6
Max generation 100
The CS algorithm optimizes the threshold value using an
optimization algorithm with the objective function of
Tsallis. Figure 1 shows the CS algorithm flow chart used
in the research for coastal image segmentation.
Using input as in Table 1 as a parameter which will then
be segmented by the CS algorithm to produce a stable
random value and optimize Tsallis's objective function by
finding the best threshold value. In a nest host there can be
two eggs, in other words the nest can store more than one
solution except to simplify the problem, a nest will only
store one solution (egg). Based on the three rules of the
basic CS steps above, the CS algorithm flowchart can be
summarized as the following pseudo code (Algorithm 1)
Start
Objektif function (t)*
Generate new population (n)
End
No
t <Generasi Maks)
or (stop the criteria)
Cuckoo search ramdomly
with Levy Flights
(Fi>Fj)
Release (pa) from the worst nest and make a new
place; Save the best solution (nest with the best
solution); Sort and find the best solution
Change j with a
new solution
Yes
No
Fig. 1. Flowchart of CS Algorithm.
Image segmentation based on threshold
Threshold selection
Set Objective function(Tsallis)
Cuckoo Search
Fig. 2. Blok diagram of CS based image segmentation.
149
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
Algorithm 1: CS Algorithm
1: Population Initialization: , ; 1, 2.... , 1, 2,....i jx i N j n (7);
2: Fitness value computation for a defined objective function
1 2; , ....T
nf x x x x x ;
3: if (ITER < MAXITER) then
4: Generate new solution space by retaining the current
best; 5: Fitness value computation; Memorize best nest;
6: if ak p then
7: Step size generation;
8: Replace worst nest by Lévy flight (11);
9: Fitness value computation; Memorize best nest;
10: Update the Counter;
11: Find best fitness value so far;
12: else
13: Retain those nests;
14: end
15: end
16: Find the optimum solutions;
4. PERFORMANCE MEASUREMENT
The performance of the Cuckoo Search algorithm in
optimizing image segmentation was measured using
several quantitative measurements. Measurement methods
used are Peak to Signal Noise Ratio (PSNR), Standard
Deviation (STD) and Feature Similarity Index (FSIM).
PSNR are used to measure noise levels from segmented
images. FSIM is a measurement method used to determine
the similarity of original image features with segmented
images [27] [38]. The values of PSNR, and FSIM can be
defined by equations:
2
10
25510logPSNR
MSE
(12)
𝑆𝑇𝐷 = 𝜎 =1
𝑁𝑁𝑖=1𝑋𝑖 − 𝜇
2 (13)
( ) ( )
( )
x X L m
x X m
S x PC xFSIM
PC x
(14)
Table 2: Quality Evaluation of segmentation after applying CS algorithm using Tsallis method
Segmentation image Histogram k Threshold PSNR STD FSIM
2 46 93 6.9032 33.2340 0.8554
4 46 93 140 187 15.3374 60.6767 0.9229
6 37 74 110 143
176 210
19.8714 64.7418 0.9431
150
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
5. Results and discussions
In order to find an optimal value for the parameter k an
threshold ranging from 2, 4 to 6 was defined based on
previous results obtained in the pilot study (visual
evaluation). This section describes the results of
experiments on coastal video image segmentation. CS
performance based on Tsallis as an objective function is
shown in Table 2. These values provide segmentation
results for three different threshold levels. The value of the
CS algorithm performance metric is evaluated. This
optimization algorithm is tested for the objective function
that Tsallis has. Experiments were conducted on
MATLAB R2018a which runs on Intel® Core TM i5 PCs
with 1.80 GHz CPU and 8 GB RAM. The test results show
that adding a threshold value can increase the PSNR and
FSIM values. The higher the PSNR shows
(a) (b) (c)
Fig. 3. Convergence of CS using Tsallis entropy method, (a) k = 2, (b) k = 4, (c) k = 6
Table 3: CS detection results using the Tsallis method
t Original image Segmentation Detection Result
M
o
r
n
i
n
g
N
o
o
n
g
A
f
t
e
r
n
o
o
151
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
the lower noise in segmented images. FSIM is used to
measure the similarity of features of original images with
segmented images. A higher FSIM value indicates the
original image features and segmented images have high
similarities.
Visually, segmented images at each threshold value are
associated with histogram values and evolution of
conformity values during the implementation of the CS
method. Therefore, to result a good regional classification,
this study uses the same threshold value (th), with 6 level
thresholds for the entire coastline detection system
segmentation process. Figure 3 shows the convergence
characteristics of coastal video image segmentation by
mapping the suitability of the number of iterations in each
threshold value. Segmentation with level threshold 2
displays the characteristics of stable convergence at the
9th iteration. While for the level threshold 4 is stable at
the 21st iteration and for the level threshold 6 is stable at
the 78th iteration. Figure 4 shows the results of CS
detection using the Tsallis entropy method showing
original images with segmented images and line detection
results. The CS algorithm combined with the objective
function of Tsallis using a test of increasing the threshold
number can affect the renewal of fitness values in fewer
iterations.
6. CONCLUSIONS
The multilevel thresholding method based on the CS
algorithm was proposed in this study. Combining
intelligent search capabilities of the CS algorithm and the
use of the objective function of Tsallis. Performance
measurement from this study uses two main parameters,
namely PSNR and FSIM.
PSNR is used to assess the quality of segmentation taking
into account between segmented images and original
images, while FSIM is used to measure the similarity of
features from original images with segmented images. A
higher FSIM value indicates the original image features
and segmented images have high similarities. This study
analyzes the performance of CS algorithms when
combined with Tsallis's objective function. The results
showed that the combination of CS algorithms and Tsallis
functions gave better results than conventional
segmentation methods.
REFERENCES
[1] Agrawal S, Panda R, Bhuyan S, Panigrahi BK. Tsallis
entropy based optimal multilevel thresholding using
cuckoo search algorithm, Swarm and Evolutionary
Computation, 2013; 11: 16–30.
[2] Akay BA. Study on particle swarm optimization and
artificial bee colony algorithms for multilevel
thresholding. Applied Soft Computing, 2013; 13(6):
3066–3091.
[3] Bhandari .A.K , Kumar .A , Chaudhary .S , Singh
.G.K.(2016), “A novel color image multilevel
thresholding based segmentation using nature inspired
optimization algorithms”, Elsevier Expert Systems With
Applications (63),112–133.
[4] Cheng H.D., Sun Y.: A hierarchical approach to color
image segmentation using homogeneity. IEEE
Transactions on Image processing vol 9 (2000) 2071-
2082.
[5] Christian Correa: A Comparison of fuzzy clustering
algorithms applied to feature extraction on vineyard.
Lecture Notes in Computer Science, Springer (2012).
[6] [6] Dellepiane, S., Laurentiis, R. De., Giordano,
F., "Coastline extraction from SAR images and a method
for the evaluation of the coastline precision," Pattern
Recognition Letters, vol. 25, no. 13, pp. 1461-1470,
2004.
[7] Descombes, X., Moctezuma, M., Maître, H., Rudant, J.
P., "Coastline detection by a Markovian segmentation on
SAR images," Signal Processing, vol. 55, no. 1, pp. 123-
132, Nov. 1996.
[8] Fogel, D. B., 1994, An introduction to simulated
evolutionary optimization, IEEE Trans. Neural Netw., 5,
1(January 1994), 3–14.
[9] Ganesan, P.; Rajini, V., : YIQ color space based satellite
image segmentation using modified FCM clustering and
histogram equalization," Advances in Electrical
Engineering (ICAEE), 2014 International Conference on
, vol., no., pp.1,5, 9-11 Jan. (2014).
[10] Ganesan, P., and V. Rajini: Segmentation and edge
detection of color images using CIELAB color space and
edge detectors. Emerging Trends in RobotiCS and
Communication Technologies (INTERACT), 2010
International Conference on. IEEE. (2010).
[11] Gerkey, B.P., Thrun, S., Gordon, G.Parallel Stochastic
Hill climbing with Small Teams. Multi-Robot Systems:
From Swarms to Intelligent Automata, Volume III,
(2005) 65-77.
[12] Ghamisi P, Couceiro MS, Benediktsson JA, Ferreira N
M F. An efficient method for segmentation of images
based on fractional calculus and natural selection. Expert
Syst. Appl., 2012; 39 (16):12407– 12417.
[13] Ghamisi P, Couceiro, MS, Benediktsson JA.
Classification of hyperspectral images with binary
fractional order Darwinian PSO and random forests.
SPIE Remote Sensing, 2013; 88920S-88920S-8.
[14] Ghamisi P, Couceiro MS, Martins, FML, Benediktsson
JA. Multilevel image segmentation based on fractional-
order Darwinian particle swarm optimization. IEEE T.
on Geoscience and Remote sensing,2014; 52(5):2382-
2394.
[15] Horng, M.-H., 2010, Multilevel minimum cross entropy
threshold selection based on the honey bee mating
optimization,” Expert Syst. Appl., 37, 6(Juny 2010),
4580–4592.
[16] Kapur, J. N., Sahoo, P. K., and Wong, A. K., 1985, A
new method for gray-level picture thresholding using the
entropy of the histogram, Comput. Vis. Graph. Image
Process., 29, 3(1985), 273–285.
152
International Journal of Computer Engineering and Information Technology (IJCEIT), Volume 11, Issue 7, July 2019 I. G. N. A. Pawana et. al
[17] Lai, C.-C., and Tseng, D.-C., 2004, A hybrid approach
using Gaussian smoothing and genetic algorithm for
multilevel thresholding, Int. J. Hybrid Intell. Syst., 1, 3–
4(2004), 143–152.
[18] Lee SU, Chung SY, Park RHA. Comparative
Performance Study 22]Techniques for Segmentation,
Computer vision, GraphiCS and Image processing, 1990;
52 (2) : 171 - 190.
[19] Liu, Y. X., Huang, H. J., Qiu, Z. F., Fan, J. Y.,
"Detecting coastline change from satellite images based
on beach slope estimation in a tidal flat," International
Journal of Applied Earth Observation and
Geoinformation, vol. 23, no. 13, pp. 165-176, Aug. 2013.
[20] Liu, Z., Li, F., Li, N., Wang, R., Zhang, H., "A Novel
Region-Merging Approach for Coastline Extraction
From Sentinel-1A IW Mode SAR Imagery," IEEE
Geoscience and Remote Sensing Letters, vol. 13, no. 3,
pp. 324-328, Mar. 2016.
[21] Maitra, M., and Chatterjee, A., 2008, A hybrid
cooperative–comprehensive learning based PSO
algorithm for image segmentation using multilevel
thresholding, Expert Syst. Appl., 34, 2(February 2008),
1341–1350.
[22] Mello, C. A., Dos Santos, T. J., Medeiros, H. R., and
Pereira, P. S., 2013, shoreline segmentation as a proxy to
coastal erosion detection, In Proceedings of the IEEE
International Conference on,Systems, Man, and
CybernetiCS (SMC), 1217–1222.
[23] Niedermeier, A., Romaneessen, E., and Lehner, S.,
"Detection of coastlines in SAR images using wavelet
methods," IEEE Transactions on Geoscience and Remote
Sensing, vol. 38, no. 5, pp. 2270-2281, Sep. 2000.
[24] Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., and
Perez-Cisneros, M., 2013, Multilevel thresholding
segmentation based on harmony search optimization, J.
Appl. Math., (2013).
[25] Otsu, N., 1979, A threshold selection method from gray-
level histograms, IEEE Trans. Syst. Man Cybern., 9,1
(1979), 62–66.
[26] Pan, J., Zheng, X. W., Sun, L., Yang, L. N., Wang, Y. L.,
Luo, H. W., & Wang, P. S. P. (2016). Image
segmentation based on 2D OTSU and simplified swarm
optimization. In 2016 International Conference on
Machine Learning and CybernetiCS (ICMLC) (Vol. 2,
pp. 1026–1030).
https://doi.org/10.1109/ICMLC.2016.7873020.
[27] Pal NR, Pal SK. A review on image segmentation
techniques, Pattern Recognition, 1993; 26 (9): 1277 –
1294.
[28] Rajinikanth V, Sri Madhava Raja N, Latha K. Optimal
Multilevel Image Thresholding: An Analysis with PSO
and BFO Algorithms. Aust. J. Basic and Appl. Sci.,
2014; 8(9): 443-454.
[29] Saeed, A.-M., and Fatima, A.-M., 2016, Coastline
Extraction using Satellite Imagery and Image processing
Techniques,” Int. J. Curr. Eng. Technol., 6, 4 (2016),
1245–1251.
[30] Sathya, P. D., and Kayalvizhi, R., 2011, Optimal
multilevel thresholding using bacterial foraging
algorithm, Expert Syst. Appl., 38, 12(2011), 15549–
15564.
[31] Sri Madhava Raja, N.; Rajinikanth, V.; and Latha, K.
Otsu Based Optimal Multilevel Image Thresholding
Using Firefly Algorithm, Modelling and Simulation in
Engineering, vol. 2014, Article ID 794574, 17 pages.
[32] Su Q, Hu Z. Color Image Quantization Algorithm Based
on Self-Adaptive Differential Evolution, Computational
Intelligence and Neuroscience, Vol. 2013, Article ID
231916, 8 pages.
[33] Suresh, S., and Lal, S., 2016, An efficient cuckoo search
algorithm based multilevel thresholding for segmentation
of satellite images using different objective functions,
Expert Syst. Appl., 58 (2016), 184–209.
[34] Tsai, W.-H. (1985). Moment-preserving thresolding: A
new approach. Computer Vision, Graphics, and Image
Processing, 29(3), 377–393.
[35] Widyantara, I.M.O, Armawan, I. N, Asana, I M.D.P,
Adnyana, I B.P., Automated Shoreline Detection
Derived From Video Imagery Using Multi Thresholding
Techniques, JATIT & LLS., vol. 97, hal 1500-1511,
Marc. 2019.
[36] Widyantara, I.M.O, Pramaita, N, Asana, I M.D.P,
Adnyana, I B.P., Pawana, I N.A, Multilevel
Thresholding for Coastal Video Image Segmentation
Based on Cuckoo Search Algorithm, ICCAI '19, April
19–22, 2019.
[37] Yang, X. and Deb, S., 2009, Cuckoo Search via Lévy
flights,” In Proceedings of the World Congress on
Nature Biologically Inspired Computing (NaBIC),
(2009), 210–214.
[38] Ye, Z., Zheng, Z., Yu, X., and Ning, X., 2005,
Automatic threshold selection based on ant colony
optimization algorithm, In Proceedings of the
International Conference on Neural Networks and Brain
(ICNN & B), 2(2005), 728–732.