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

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.

Symbiotic Organisms Search Optimization for Multilevel Image Thresholding

2020 ◽  
Vol 11 (2) ◽  
pp. 31-61
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Symbiotic Relationship ◽  
Symbiotic Organisms Search ◽  
Image Thresholding ◽  
Prime Concern ◽  
Feature Similarity Index ◽  
Symbiotic Organisms

Determination of optimum thresholds is the prime concern of any multilevel image thresholding technique. The traditional methods for multilevel thresholding are computationally expensive, time-consuming, and also suffer from lack of accuracy and stability. To address this issue, the authors propose a new methodology for multilevel image thresholding based on a recently developed meta-heuristic algorithm, Symbiotic Organisms Search (SOS). The SOS algorithm has been inspired by the symbiotic relationship among the organism in nature. This article has utilized the concept of the symbiotic relationship among the organisms to optimize three objective functions: Otsu's between class variance and Kapur's and Tsallis entropy for image segmentation. The performance of the SOS based image segmentation algorithm has been evaluated using a set of benchmark images and has been compared with four recent meta-heuristic algorithms. The algorithms are compared in terms of effectiveness and consistency. The quality of the algorithms has been estimated by some well-defined quality metrics such as peak signal-to-noise ratio (PSNR), structure similarity index (SSIM), and, feature similarity index (FSIM). The experimental results of the algorithms reveal that the balance of intensification and diversification of the SOS algorithm to achieve the global optima is better than others.

Elephant Herding Optimization for Multi-Level Image Thresholding

2020 ◽  
Vol 11 (4) ◽  
pp. 64-90
Author(s):  
Falguni Chakraborty ◽  
Provas Kumar Roy ◽  
Debashis Nandi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Cuckoo Search ◽  
Threshold Value ◽  
Objective Functions ◽  
Image Thresholding ◽  
Bee Colony ◽  
Kapur’S Entropy ◽  
Feature Similarity Index

Multilevel thresholding plays a significant role in the arena of image segmentation. The main issue of multilevel image thresholding is to select the optimal combination of threshold value at different level. However, this problem has become challenging with the higher number of levels, because computational complexity is increased exponentially as the increase of number of threshold. To address this problem, this paper has proposed elephant herding optimization (EHO) based multilevel image thresholding technique for image segmentation. The EHO method has been inspired by the herding behaviour of elephant group in nature. Two well-known objective functions such as ‘Kapur's entropy' and ‘between-class variance method' have been used to determine the optimized threshold values for segmentation of different objects from an image. The performance of the proposed algorithm has been verified using a set of different test images taken from a well-known benchmark dataset named Berkeley Segmentation Dataset (BSDS). For comparative analysis, the results have been compared with three popular algorithms, e.g. cuckoo search (CS), artificial bee colony (ABC) and particle swarm optimization (PSO). It has been observed that the performance of the proposed EHO based image segmentation technique is efficient and promising with respect to the others in terms of the values of optimized thresholds, objective functions, peak signal-to-noise ratio (PSNR), structure similarity index (SSIM) and feature similarity index (FSIM). The algorithm also shows better convergence profile than the other methods discussed.

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

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 716 ◽  
Author(s):  
Xiaoli Bao ◽  
Heming Jia ◽  
Chunbo Lang
Keyword(s):  
Image Segmentation ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Similarity Index ◽  
Search Space ◽  
Color Image Segmentation ◽  
Computational Time ◽  
Multilevel Thresholding ◽  
Dragonfly Algorithm ◽  
Opposition Based Learning

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.

scholarly journals Enhanced Slime Mould Algorithm for Multilevel Thresholding Image Segmentation Using Entropy Measures

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1700
Author(s):  
Shanying Lin ◽  
Heming Jia ◽  
Laith Abualigah ◽  
Maryam Altalhi
Keyword(s):  
Image Segmentation ◽  
Signal To Noise Ratio ◽  
Fitness Function ◽  
Similarity Index ◽  
Multilevel Thresholding ◽  
Slime Mould ◽  
Opposition Based Learning ◽  
Wilcoxon Rank Sum Test ◽  
Entropy Measures ◽  
Feature Similarity Index

Image segmentation is a fundamental but essential step in image processing because it dramatically influences posterior image analysis. Multilevel thresholding image segmentation is one of the most popular image segmentation techniques, and many researchers have used meta-heuristic optimization algorithms (MAs) to determine the threshold values. However, MAs have some defects; for example, they are prone to stagnate in local optimal and slow convergence speed. This paper proposes an enhanced slime mould algorithm for global optimization and multilevel thresholding image segmentation, namely ESMA. First, the Levy flight method is used to improve the exploration ability of SMA. Second, quasi opposition-based learning is introduced to enhance the exploitation ability and balance the exploration and exploitation. Then, the superiority of the proposed work ESMA is confirmed concerning the 23 benchmark functions. Afterward, the ESMA is applied in multilevel thresholding image segmentation using minimum cross-entropy as the fitness function. We select eight greyscale images as the benchmark images for testing and compare them with the other classical and state-of-the-art algorithms. Meanwhile, the experimental metrics include the average fitness (mean), standard deviation (Std), peak signal to noise ratio (PSNR), structure similarity index (SSIM), feature similarity index (FSIM), and Wilcoxon rank-sum test, which is utilized to evaluate the quality of segmentation. Experimental results demonstrated that ESMA is superior to other algorithms and can provide higher segmentation accuracy.

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

Entropy ◽  
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.

scholarly journals Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

2019 ◽  
Vol 11 (9) ◽  
pp. 1134 ◽  
Author(s):  
Heming Jia ◽  
Chunbo Lang ◽  
Diego Oliva ◽  
Wenlong Song ◽  
Xiaoxu Peng
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Satellite Image ◽  
Similarity Index ◽  
Image Thresholding ◽  
Grasshopper Optimization Algorithm ◽  
Significant Difference ◽  
Grasshopper Optimization

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

scholarly journals Color Image Generation from Range and Reflection Data of LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5414
Author(s):  
Hyun-Koo Kim ◽  
Kook-Yeol Yoo ◽  
Ho-Youl Jung
Keyword(s):  
Color Image ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Point Clouds ◽  
Range Data ◽  
Fusion Method ◽  
Image Generation ◽  
Learning Networks ◽  
Reflection Data

Recently, it has been reported that a camera-captured-like color image can be generated from the reflection data of 3D light detection and ranging (LiDAR). In this paper, we present that the color image can also be generated from the range data of LiDAR. We propose deep learning networks that generate color images by fusing reflection and range data from LiDAR point clouds. In the proposed networks, the two datasets are fused in three ways—early, mid, and last fusion techniques. The baseline network is the encoder-decoder structured fully convolution network (ED-FCN). The image generation performances were evaluated according to source types, including reflection data-only, range data-only, and fusion of the two datasets. The well-known KITTI evaluation data were used for training and verification. The simulation results showed that the proposed last fusion method yields improvements of 0.53 dB, 0.49 dB, and 0.02 in gray-scale peak signal-to-noise ratio (PSNR), color-scale PSNR, and structural similarity index measure (SSIM), respectively, over the conventional reflection-based ED-FCN. Besides, the last fusion method can be applied to real-time applications with an average processing time of 13.56 ms per frame. The methodology presented in this paper would be a powerful tool for generating data from two or more heterogeneous sources.

scholarly journals Multilevel Image Thresholding for Image Segmentation using Hybrid Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 4272-4279
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Similarity Index ◽  
Structural Similarity ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Entropy Maximization ◽  
Image Thresholding ◽  
Segmentation Threshold

Image thresholding is an extraction method of objects from a background scene, which is used most of the time to evaluate and interpret images because of their advanced simplicity, robustness, time reduced, and precision. The main objective is to distinguish the subject from the background of the image segmentation. As the ordinary image segmentation threshold approach is computerized costly while the necessity for optimization techniques are highly recommended for multi-tier image thresholds. Level object segmentation threshold by using Shannon entropy and Fuzzy entropy maximized with hGSA-PS. An entropy maximization of hGSA-PS dependent multilevel image thresholds is developed, where the results are best demonstrated in PSNR, misclassification, structural similarity index and segmented image quality compared to the Firefly algorithm, adaptive cuckoo search algorithm and the search algorithm gravitational.

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