Multilevel thresholding selection based on the artificial bee colony algorithm for image segmentation
TL;DR: The experimental results demonstrate that the proposed maximum entropy based artificial bee colony thresholding (MEABCT) algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method.
Abstract: Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the artificial bee colony (ABC) algorithm is proposed: the maximum entropy based artificial bee colony thresholding (MEABCT) method. Four different methods are compared to this proposed method: the particle swarm optimization (PSO), the hybrid cooperative-comprehensive learning based PSO algorithm (HCOCLPSO), the Fast Otsu’s method and the honey bee mating optimization (HBMO). The experimental results demonstrate that the proposed MEABCT algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method. Compared to the other four thresholding methods, the segmentation results of using the MEABCT algorithm is the most, however, the computation time by using the MEABCT algorithm is shorter than that of the other four methods.
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TL;DR: This work presents a comprehensive survey of the advances with ABC and its applications and it is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
Abstract: Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.
1,645 citations
Cites methods from "Multilevel thresholding selection b..."
...A hybrid simplex ABC algorithm which combines Nelder-Mead simplex method with ABC was introduced and used to improve the search efficiency in computation by Kang et al. (2009a) and used for inverse analysis problems by Kang et al....
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...A hybrid simplex ABC algorithm which combines Nelder-Mead simplex method with ABC was introduced and used to improve the search efficiency in computation by Kang et al. (2009a) and used for inverse analysis problems by Kang et al. (2009b). Marinakis et al....
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...Karaboga (2009) presented a new design method based on ABC algorithm for digital IIR filters and its performance was compared with that of a conventional optimization algorithm (LSQ-nonlin) and PSO algorithm....
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...A hybrid simplex ABC algorithm which combines Nelder-Mead simplex method with ABC was introduced and used to improve the search efficiency in computation by Kang et al. (2009a) and used for inverse analysis problems by Kang et al. (2009b). Marinakis et al. (2009) presented a new hybrid algorithm, which is based on the concepts of ABC and greedy randomized adaptive search procedure, for optimally clustering n objects into k clusters....
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TL;DR: The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.
Abstract: Two metaheuristic algorithms (WOA and MFO) are used.These algorithms are applied to multilevel thresholding image segmentation.MFO and WOA are better than compared algorithms.MFO is better than WOA for higher number of thresholds. Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsus fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.
431 citations
TL;DR: The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
Abstract: The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.
392 citations
Cites methods from "Multilevel thresholding selection b..."
...In which, bi-level thresholding selects only one threshold to separate the pixel values into two distinct classes where multilevel thresholding used to perform multiple thresholds through which pixels can be divided into several groups (Horng, 2011)....
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...…2013; Maitra & Chatterjee, 2008; Yin, 2007), artificial bee colony (ABC) (Akay, 2013; Cuevas, Sencion, Zaldivar, Perez-Cisneros, & Sossa, 2012; Horng, 2011; Horng & Jiang, 2010; Zhang & Wu, 2011), ant colony optimization (ACO) (Tao, Jin, & Liu, 2007; Ye et al., 2005), bacterial foraging (BF)…...
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TL;DR: Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
Abstract: A modified ABC algorithm based fast satellite image segmentation has been presented.ABC, PSO and GA methods are compared with this proposed method.The experimental results demonstrate better performance of MABC based technique.The proposed MABC based approach is much faster (CPU time is less).The validity of the proposed technique is reported both qualitatively and quantitatively. In this paper, a modified artificial bee colony (MABC) algorithm based satellite image segmentation using different objective function has been presented to find the optimal multilevel thresholds. Three different methods are compared with this proposed method such as ABC, particle swarm optimization (PSO) and genetic algorithm (GA) using Kapur's, Otsu and Tsallis objective function for optimal multilevel thresholding. The experimental results demonstrate that the proposed MABC algorithm based segmentation can efficiently and accurately search multilevel thresholds, which are very close to optimal ones examined by the exhaustive search method. In MABC algorithm, an improved solution search equation is used which is based on the bee's search only around the best solution of previous iteration to improve exploitation. In addition, to improve global convergence when generating initial population, both chaotic system and opposition-based learning method are employed. Compared to other thresholding methods, segmentation results of the proposed MABC algorithm is most promising, and the computational time is also minimized.
289 citations
TL;DR: The proposed algorithm, integrated and improved with search strategies, outperforms the basic variants and other variants of the ABC algorithm and other methods in terms of solution quality and robustness for most of the experiments.
213 citations
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37,017 citations
TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Abstract: Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
6,377 citations
01 Mar 1985-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput.16, 1981, 210–239) have been carefully and critically examined and a new method with a sound theoretical foundation is proposed.
Abstract: Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput. Graphics Image Process.16, 1981, 210–239) have been carefully and critically examined. A new method with a sound theoretical foundation is proposed. Examples are given on a number of real and artifically generated histograms.
3,551 citations
01 Jan 2008
TL;DR: The simulation results show that the performance of ABC algorithm is comparable to those of differential evolution, particle swarm optimization and evolutionary algorithm and can be efficiently employed to solve engineering problems with high dimensionality.
Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
3,242 citations
01 Feb 1988-Graphical Models \/graphical Models and Image Processing \/computer Vision, Graphics, and Image Processing
TL;DR: This paper presents a survey of thresholding techniques and attempts to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures.
Abstract: In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
2,771 citations