Dragonfly Algorithm with Opposition-Based Learning for Multilevel Thresholding Color Image Segmentation

Multilevel thresholding is a very active research field in image segmentation, and has been successfully used in various applications. However, the computational time will increase exponentially as the number of thresholds increases, and for color images which contain more information this is even worse. To overcome the drawback while maintaining segmentation accuracy, a modified version of dragonfly algorithm (DA) with opposition-based learning (OBLDA) for color image segmentation is proposed in this paper. The opposition-based learning (OBL) strategy simultaneously considers the current solution and the opposite solution, which are symmetrical in the search space. With the introduction of OBL, the proposed algorithm has a faster convergence speed and more balanced exploration–exploitation compared with the original DA. In order to clearly demonstrate the outstanding performance of the OBLDA, the proposed method is compared with seven state-of-the-art meta-heuristic algorithms, through experiments on 10 test images. The optimal threshold values are calculated by the maximization of between-class variance and Kapur’s entropy. Meanwhile, some indicators, including peak signal to noise ratio (PSNR), feature similarity index (FSIM), structure similarity index (SSIM), the average fitness values, standard deviation (STD), and computation time are used as evaluation criteria in the experiments. The promising results reveal that proposed method has the advantages of high accuracy and remarkable stability. Wilcoxon’s rank sum test and Friedman test are also performed to verify the superiority of OBLDA in a statistical way. Furthermore, various satellite images are also included for robustness testing. In conclusion, the OBLDA algorithm is a feasible and effective method for multilevel thresholding color image segmentation.

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A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽

10.3390/e21040398 ◽

2019 ◽

Vol 21 (4) ◽

pp. 398 ◽

Cited By ~ 5

Author(s):

Suhang Song ◽

Heming Jia ◽

Jun Ma

Keyword(s):

Electromagnetic Field ◽

Image Segmentation ◽

Heuristic Algorithms ◽

Color Image ◽

Fitness Function ◽

Fuzzy Entropy ◽

Color Image Segmentation ◽

Superior Performance ◽

Computational Time ◽

Multilevel Thresholding

Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.

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Kapur’s Entropy for Color Image Segmentation Based on a Hybrid Whale Optimization Algorithm

Entropy ◽

10.3390/e21030318 ◽

2019 ◽

Vol 21 (3) ◽

pp. 318 ◽

Cited By ~ 9

Author(s):

Chunbo Lang ◽

Heming Jia

Keyword(s):

Image Segmentation ◽

Optimization Algorithm ◽

Heuristic Algorithms ◽

Color Image ◽

Similarity Index ◽

Whale Optimization Algorithm ◽

Color Image Segmentation ◽

De Algorithm ◽

Whale Optimization ◽

Kapur’S Entropy

In this paper, a new hybrid whale optimization algorithm (WOA) called WOA-DE is proposed to better balance the exploitation and exploration phases of optimization. Differential evolution (DE) is adopted as a local search strategy with the purpose of enhancing exploitation capability. The WOA-DE algorithm is then utilized to solve the problem of multilevel color image segmentation that can be considered as a challenging optimization task. Kapur’s entropy is used to obtain an efficient image segmentation method. In order to evaluate the performance of proposed algorithm, different images are selected for experiments, including natural images, satellite images and magnetic resonance (MR) images. The experimental results are compared with state-of-the-art meta-heuristic algorithms as well as conventional approaches. Several performance measures have been used such as average fitness values, standard deviation (STD), peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), Wilcoxon’s rank sum test, and Friedman test. The experimental results indicate that the WOA-DE algorithm is superior to the other meta-heuristic algorithms. In addition, to show the effectiveness of the proposed technique, the Otsu method is used for comparison.

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Multilevel thresholding using a modified ant lion optimizer with opposition-based learning for color image segmentation

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021155 ◽

2021 ◽

Vol 18 (4) ◽

pp. 3092-3143

Author(s):

Shikai Wang ◽

◽

Kangjian Sun ◽

Wanying Zhang ◽

Heming Jia ◽

...

Keyword(s):

Image Segmentation ◽

Color Image ◽

Color Image Segmentation ◽

Multilevel Thresholding ◽

Opposition Based Learning ◽

Ant Lion Optimizer ◽

Ant Lion

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A Hybrid Preaching Optimization Algorithm Based on Kapur Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽

10.3390/e23121599 ◽

2021 ◽

Vol 23 (12) ◽

pp. 1599

Author(s):

Bowen Wu ◽

Liangkuan Zhu ◽

Jun Cao ◽

Jingyu Wang

Keyword(s):

Image Segmentation ◽

Time Delay ◽

Optimization Algorithm ◽

Color Image ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Structural Similarity ◽

Color Image Segmentation ◽

Multilevel Thresholding ◽

Evolutionary State

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation.

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Color Image Segmentation by Multilevel Thresholding Based on Harmony Search Algorithm

Lecture Notes in Computer Science - Intelligent Data Engineering and Automated Learning – IDEAL 2017 ◽

10.1007/978-3-319-68935-7_62 ◽

2017 ◽

pp. 571-579

Author(s):

Viktor Tuba ◽

Marko Beko ◽

Milan Tuba

Keyword(s):

Image Segmentation ◽

Color Image ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Color Image Segmentation ◽

Multilevel Thresholding

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A Semiautomatic Multi-Label Color Image Segmentation Coupling Dirichlet Problem and Colour Distances

Journal of Imaging ◽

10.3390/jimaging7100208 ◽

2021 ◽

Vol 7 (10) ◽

pp. 208

Author(s):

Giacomo Aletti ◽

Alessandro Benfenati ◽

Giovanni Naldi

Keyword(s):

Image Analysis ◽

Image Segmentation ◽

Random Walk ◽

Dirichlet Problem ◽

Color Image ◽

Color Image Segmentation ◽

Computational Time ◽

Random Walker ◽

Segmentation Process ◽

Colour Distance

Image segmentation is an essential but critical component in low level vision, image analysis, pattern recognition, and now in robotic systems. In addition, it is one of the most challenging tasks in image processing and determines the quality of the final results of the image analysis. Colour based segmentation could hence offer more significant extraction of information as compared to intensity or texture based segmentation. In this work, we propose a new local or global method for multi-label segmentation that combines a random walk based model with a direct label assignment computed using a suitable colour distance. Our approach is a semi-automatic image segmentation technique, since it requires user interaction for the initialisation of the segmentation process. The random walk part involves a combinatorial Dirichlet problem for a weighted graph, where the nodes are the pixel of the image, and the positive weights are related to the distances between pixels: in this work we propose a novel colour distance for computing such weights. In the random walker model we assign to each pixel of the image a probability quantifying the likelihood that the node belongs to some subregion. The computation of the colour distance is pursued by employing the coordinates in a colour space (e.g., RGB, XYZ, YCbCr) of a pixel and of the ones in its neighbourhood (e.g., in a 8–neighbourhood). The segmentation process is, therefore, reduced to an optimisation problem coupling the probabilities from the random walker approach, and the similarity with respect the labelled pixels. A further investigation involves an adaptive preprocess strategy using a regression tree for learning suitable weights to be used in the computation of the colour distance. We discuss the properties of the new method also by comparing with standard random walk and k−means approaches. The experimental results carried on the White Blood Cell (WBC) dataset and GrabCut datasets show the remarkable performance of the proposed method in comparison with state-of-the-art methods, such as normalised random walk and normalised lazy random walk, with respect to segmentation quality and computational time. Moreover, it reveals to be very robust with respect to the presence of noise and to the choice of the colourspace.

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Application of Opposition Based Improved Firefly Algorithm in Multi-Level Image Thresholding of Color Images using Between Class Variance

Recent Patents on Computer Science ◽

10.2174/2213275912666190716165024 ◽

2019 ◽

Vol 12 ◽

Author(s):

Abhay Sharma ◽

Rekha Chaturvedi ◽

Umesh Dwivedi ◽

Sandeep Kumar

Keyword(s):

Image Segmentation ◽

Firefly Algorithm ◽

Color Image ◽

Signal To Noise Ratio ◽

Similarity Index ◽

Image Thresholding ◽

Opposition Based Learning ◽

Improved Firefly Algorithm ◽

Engineering Problems ◽

Multi Level

Background: Image segmentation is the fundamental step in image processing. Multi-level image segmentation for color image is a very complex and time-consuming process which can be defined as non-deterministic optimization problem. Nature inspired meta-heuristics are best suited to solve such problems. Though several algorithms exist; a modification to suit certain class of engineering problems is always welcome. Objective: This paper provides a modified firefly algorithm and its uses for multilevel thresholding in colored images. Opposition based learning is incorporated in the firefly algorithm to improve convergence rate and robustness. Between class variance method of thresholding is used to formulate the objective function. Method: Numerous benchmark images are tested for evaluating the performance of proposed method. Results: The Experimental results validate the performance of Opposition based improved firefly algorithm (OBIFA) for multi-level image segmentation using peak signal to noise ratio (PSNR) and structured similarity index metric (SSIM)parameter. Conclusion: The OBIFA algorithm is best suited for multilevel image thresholding. It provides best results compared to Darwinian Particle Swarm Optimization (DPSO) and Electro magnetism optimization (EMO) for the parameter: convergence speed, PSNR and SSIM values.

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