Color Image Segmentation By Cuckoo Search

Nandy, Sudarshan; Yang, Xin‐She; Sarkar, Partha Pratim; Das, Achintya

doi:10.1080/10798587.2015.1025480

Cited by 17 publications

(11 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In color image segmentation, Ye et al (2003) proposed image segmentation method based on density based clustering. Nandy et al (2015) proposed color image segmentation method optimized by Cuckoo Search algorithm CS. The researchers used CS algorithm to Pertanika J. Sci.…”

Section: Literature Reviewmentioning

confidence: 99%

“…& Technol. 28 (4): 1389 -1411 (2020) cluster the image after finding optimal centers (Nandy et al, 2015). Mathur and Purohit (2014) developed color image segmentation methods using k-means clustering algorithm, where, it classified the image into different clusters depending on Euclidean distance metric.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Clustering can be defined as a process of splitting homogeneous objects in one cluster and heterogeneous objects in another cluster (Kapoor et al, 2017). In literature, there are different methods of clustering based such as: k-means clustering (Dhanachandra et al, 2015;Mathur & Purohit, 2014), DBScan clustering method (Ye et al, 2003), subtractive clustering (Dhanachandra et al, 2015), and most recently used intelligent inspired optimization algorithms (i.e.meta-heuristic algorithms) in clustering like Wolf optimization algorithm (Kapoor et al, 2017), Cuckoo search algorithm (Nandy et al, 2015), Particle Swarm Optimization (Zhang et al, 2011), Bat optimization algorithm (Alagarsamy et al, 2019;Mishra & Panda, 2018;Yang, 2010) and most recently introduced Whale Optimization algorithm (Mirjalili & Lewis, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

Mohammed¹,

Ghathwan²

2020

JST

View full text Add to dashboard Cite

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algorithms have been adopted to enhance the clustering performance, some algorithms still need improvements. Whale optimization algorithm (WOA) is recognized to be enough competition with common meta-heuristic algorithms, where it has an ability to obtain a global optimal solution and avoid local optima. In this paper, a new method for color image based segmentation is proposed based on using whale optimization algorithm in clustering. The proposed method is called the whale color image based segmentation (WhCIbS). It was used to divide the color image into a predefined number of clusters. The input image in RGB color space was converted into L*a*b color space. Comparison of the proposed WhCIbS method was performed with the wolf color image based segmentation, cuckoo color image based segmentation, bat color image based segmentation, and k-means color image based segmentation over four benchmark color images. Experimental results demonstrated that the proposed WhCIbS had higher value of PSNR and lower value of RMSR in most cases compared to other methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

Mohammed¹,

Ghathwan²

2020

JST

View full text Add to dashboard Cite

show abstract

“…These algorithms are used to solve real-world applications (Yang and He, 2016;Pradhan et al, 2018). These applications are divided into several areas, such as structural optimization (Obadage and Harnpornchai, 2006;Limbourg and Kochs, 2006;Yang and Deb, 2010;Yang, 2011;Yang and Gandomi, 2012;Bekdas et al, 2015), scheduling and routing (Marichelvam et al, 2014), software testing (Srivatsava et al, 2013), image processing (Nandy et al, 2015) and data mining (Abualigah et al, 2018;Niu et al, 2018). These areas of problems have been solved by a human in different ways either heuristic or metaheuristic ways.…”

Section: Introductionmentioning

confidence: 99%

A new K-means grey wolf algorithm for engineering problems

Mohammed¹,

Abdul²,

Rashid

et al. 2021

WJE

View full text Add to dashboard Cite

Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.

show abstract

“…It is important to mention that, a FIS needs to have an optimal structure to obtain better results, and this can be achieved using the knowledge of an expert or some optimization technique [4,16], examples of the structural information are parameters of the membership functions and fuzzy if-then rules. Among some of these techniques applied to fuzzy inference system optimization, we can find; genetic algorithms (GAs) [14], particle swarm optimization (PSO) [36], cuckoo optimization algorithm (COA) [24], the bat algorithm (BA) [8] and chemical reaction optimization (CRO) [1].…”

Section: Introductionmentioning

confidence: 99%

Genetic optimization of type-1, interval and intuitionistic fuzzy recognition systems

Melín¹

2018

NIFS

View full text Add to dashboard Cite

In this paper a new method for fuzzy system optimization is presented. The proposed method performs the intuitionistic or type-2 fuzzy inference system design using a hierarchical genetic algorithm as an optimization method. This method is an improvement of a fuzzy system optimization approach presented in previous works where only the optimization of type-1 and interval type-2 fuzzy inference systems was performed considering a human recognition application. Human recognition is performed using three biometric measures namely iris, ear, and voice, where the main idea is to perform the combination of responses in modular neural networks using an optimized fuzzy inference system to improve the final results without and with noisy conditions. The results obtained show the effectiveness of the proposed method for designing optimal structures of fuzzy systems. The design of optimal structures of fuzzy systems include among other parameters; type of fuzzy logic (Type-1, interval type-2 and intuitionistic fuzzy logic), type of model (Mamdani model or Sugeno model), and consequents of the fuzzy if-then rules.

show abstract

Color Image Segmentation By Cuckoo Search

Cited by 17 publications

References 29 publications

Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

A new K-means grey wolf algorithm for engineering problems

Genetic optimization of type-1, interval and intuitionistic fuzzy recognition systems

Contact Info

Product

Resources

About