Image segmentation using firefly algorithm

Sharma, Akash; Sehgal, Smriti

doi:10.1109/incite.2016.7857598

Cited by 16 publications

(6 citation statements)

References 5 publications

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“…With this procedure, Evolutionary Algorithms (EA) are considered with improvements in points such as issue and determination independence, nearby optima evasion, and straightforwardness. Many of these algorithms for example, Firefly Algorithm (FA) [53], GA [54], PSO [24], ABC [55], ALO [56], GWO [57,58], ACO [59] and others have been implemented in image segmentation. Among them, in the study using ALO, Mostafa et al [56] proposed a methodology for liver segmentation based on ALO.…”

Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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Image segmentation is a significant step in image processing that applies to various fields. These fields include machine vision, object detection, astronomy, biometric recognition systems (face, fingerprint, plate, and eye), medical imaging, video surveillance, and many other image-based technologies. Efficient image segmentation is one of the most important tasks and critical roles in automatic image processing. Especially in engineering studies, finding the most suitable solutions for problems is one of the important research topics. Bio-inspired algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BAT), etc. are used to find the optimal solutions in search spaces and Ant Lion Optimization (ALO) is one of these algorithms. In recent years, bio-inspired algorithms are used to optimize the segmentation parameters of the images. This research proposes a modified region growing (RG) image segmentation approach using bio-inspired ALO. Region growing (RG) has three main problems as the selection of the right seeds, the number of seeds, and the region growing strategy. Therefore, ALO was used to solve seed selection problems in RG. In this study, firstly, the median filter was applied to the inputs to improve the quality of the images. Subsequently, the region growing segmentation was carried out using optimal seed points obtained from the ALO. For obtaining the optimal seeds, ALO was used to solve the limitations of RG during the segmentation process. The success of the proposed approach was tested using some images taken from the BSDS300 (Berkeley) dataset. The experimental results show that the proposed method segments almost all the images.

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Section: Related Workmentioning

confidence: 99%

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Jama¹,

Baykan²

2020

European Journal of Science and Technology

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“…The Firefly algorithm developed by Xin-She Yang [20] belongs to a meta-heuristic nature inspired class of algorithms, based on swarm intelligence, being typically used to solve a variety of optimization problems in real-world applications, from project scheduling problems to rich vehicle routing, and from image processing to engineering machining parameters optimization [21][22][23][24].…”

Section: The Firefly Algorithmmentioning

confidence: 99%

New Evolutionary-Based Techniques for Image Registration

Cocianu¹,

Stan²

2019

Applied Sciences

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The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.

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“…On the other hand, equation 2 is used to compute the brightness of each firefly which will define the value of the firefly's attractiveness [18] [19]. In the equation, the distance between the two fireflies is denoted by 'r' and β0 defines their attractiveness at r=0.…”

Section: B Firefly Algorithmmentioning

confidence: 99%

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

Masangcap¹

2018

IJRASET

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and high computational cost. Therefore, these remain the need to enhance EM algorithm in its initialization stage to produce good clustering results and to take less computational time. Since good initialization leads to fast convergence, there is a challenge of improving the initialization stage of the EM Algorithm. With that, this study aims to improve the Expectation-Maximization (EM) algorithm based on the initial parameters selection. An improved initial parameters selection technique for EM algorithm is introduced in this study which uses the concept of firefly movement and light intensity of firefly algorithm. A comparison of the clustering performance of the Enhanced EM algorithm (EnEM) to the Standard EM algorithm (StEM) in terms of clustering fitness, clustering error and computing time of the two algorithms is discussed. The application of an enhanced EM algorithm as a clustering method will open more opportunities to discover new knowledge as an outcome of a more precise data analysis.

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Image segmentation using firefly algorithm

Cited by 16 publications

References 5 publications

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

Karınca Aslanı Optimizasyon Algoritması Kullanarak Görüntü Segmentasyonu İçin Modifiye Edilmiş Bölge Genişletme Yöntemi

New Evolutionary-Based Techniques for Image Registration

Improved Expectation Maximization (EM) Algorithm based on Initial Parameter Selection

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