Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation

Dhal, Krishna Gopal; Das, Arunita; Ray, Swarnajit; Gálvez, Jorge; Das, Sanjoy

doi:10.1007/s11831-019-09334-y

Cited by 80 publications

(45 citation statements)

References 263 publications

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“…In the segmentation process of fuzzy shareholding, regions are allowed to partially overlap, and the affiliation function assigns probabilities for pixels to belong to each region. is soft-threshold segmentation method can retain more information about the original grayscale image during the segmentation process, effectively avoiding the miss mentation caused by artifacts and improving the effectiveness of grayscale image segmentation [13].…”

Section: Grayscale Image Reshold Segmentation Selectionsmentioning

confidence: 99%

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Chai

2021

Complexity

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

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Section: Grayscale Image Reshold Segmentation Selectionsmentioning

confidence: 99%

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Chai

2021

Complexity

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“…In addition to common control parameters, there are some diverse parameters to be set for some of the algorithms tested in this section. The control parameters settings for the five algorithms in the experiments are given in Table (1). All the experiments are implemented using Matlab R2018a in a computer running on Windows 7 system with 3.4 GHz Intel core-i7 CPU and 16 GB RAM.…”

Section: A Experimental Settingsmentioning

confidence: 99%

“…Image segmentation is an essential technique for image processing, which is aiming to partition an image into a number of congeneric regions with similar characteristics using some pre-defined measurement criterions. Among various popular image segmentation methods, thresholding is one of the most efficient and easiest methods which are used commonly and extensively [1]. If a grayscale image is separated into two classes by one threshold value based on the histogram, the process is called bi-level thresholding.…”

Section: Introductionmentioning

confidence: 99%

Bee Foraging Algorithm Based Multi-Level Thresholding For Image Segmentation

Zhang

Yin

2020

IEEE Access

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Multi-level thresholding is one of the essential approaches for image segmentation. Determining the optimal thresholds for multi-level thresholding needs exhaustive searching which is time-consuming. To improve the searching efficiency, a novel population based bee foraging algorithm (BFA) for multi-level thresholding is presented in this paper. The proposed algorithm provides different flying trajectories for different types of bees and takes both single-dimensional and multi-dimensional search aiming to maintain a proper balance between exploitation and exploration. The bee swarm is divided into a number of sub-swarms to enhance the diversity. A neighbourhood shrinking strategy is applied to mitigate stagnation and accelerate convergence. Experiments have been performed on eight benchmark images using between-class variance as the thresholding criterion. The performance of the proposed algorithm is compared with some state-of-art meta-heuristic algorithms. The results show that BFA is efficient and robust, produces excellent results with few control parameters, and outperforms other algorithms investigated in this consideration on most of the tested images. INDEX TERMS Bee foraging algorithm, image segmentation, meta-heuristic, multi-level thresholding.

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“…Image processing without a vision system is illadvised, and a proper pre-processing improves the accuracy of the results. Segmentation pre-processing facilitates the representation and analysis of images [35], and must be accurately performed in any vision application [36]. In particular, the image should be subdivided to extract only the regions carrying useful information.…”

Section: Introductionmentioning

confidence: 99%

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

et al. 2021

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This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA-LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA-LEO was verified on the CEC'2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA-LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA-LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings. INDEX TERMSMetaheuristic algorithms; Tunicate swarm algorithm (TSA); Local escaping operator (LEO); Multilevel thresholding; Image segmentation; Kapur's entropy and Otsu method.

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Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation

Cited by 80 publications

References 263 publications

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Bee Foraging Algorithm Based Multi-Level Thresholding For Image Segmentation

An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

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