2D Tsallis Entropy for Image Segmentation Based on Modified Chaotic Bat Algorithm

Ye, Zhiwei; Yang, Juan; Wang, Mingwei; Zong, Xinlu; Yan, Lingyu; Liu, Wei

doi:10.3390/e20040239

Cited by 17 publications

(9 citation statements)

References 60 publications

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“…However, the idea of extending the dimension of the histogram using the correlation of the neighboring pixels is still heuristic. It is of great interest to extend Equation ( 29) into two, or even higher, dimensions of the histogram because the development of optimization algorithms [15], refs. [19,20] can effectively reduce the computational cost.…”

Section: Resultsmentioning

confidence: 99%

“…The entropy-based algorithm [10][11][12][13][14] is another option for image segmentation since the gray-level histogram can be considered as a kind of probability distribution, and maximization of the corresponding entropies is a nature-inspired means of finding the optimal thresholds. In order to improve the robustness and anti-interference of the thresholding algorithms, two-dimensional histogram distributions [15][16][17] are frequently used to detect the edges and noise of the images, and thus achieve better segmentation results [18][19][20]. It is worth mentioning that, among these entropy-based algorithms, "Tsallis entropybased thresholding" introduces the concept of nonextensivity into the image segmentation field [21,22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution

Deng

Shi

2022

Entropy

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In order to automatically recognize different kinds of objects from their backgrounds, a self-adaptive segmentation algorithm that can effectively extract the targets from various surroundings is of great importance. Image thresholding is widely adopted in this field because of its simplicity and high efficiency. The entropy-based and variance-based algorithms are two main kinds of image thresholding methods, and have been independently developed for different kinds of images over the years. In this paper, their advantages are combined and a new algorithm is proposed to deal with a more general scope of images, including the long-range correlations among the pixels that can be determined by a nonextensive parameter. In comparison with the other famous entropy-based and variance-based image thresholding algorithms, the new algorithm performs better in terms of correctness and robustness, as quantitatively demonstrated by four quality indices, ME, RAE, MHD, and PSNR. Furthermore, the whole process of the new algorithm has potential application in self-adaptive object recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Image Thresholding within Nonextensive Entropy and the Variance of the Gray-Level Distribution

Deng

Shi

2022

Entropy

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“…Chaos can be utilized to support an optimization technique to investigate new areas dynamically in search space. The chaos theory is utilized in BA to enhance the solution heterogeneity [ 51 , 312 , 323 , 325 ].…”

Section: Recent Variants Of Bat-inspired Algorithmmentioning

confidence: 99%

“…A modified chaotic BA for image segmentation by utilizing 2D Tsallis entropy was proposed in [ 323 ]. The proposed algorithm is parameter-free as it utilized a random walk style search using Levy flights.…”

Section: Recent Variants Of Bat-inspired Algorithmmentioning

confidence: 99%

Recent advances of bat-inspired algorithm, its versions and applications

Alyasseri

Alomari

Al-Betar

et al. 2022

Neural Comput & Applic

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Bat-inspired algorithm (BA) is a robust swarm intelligence algorithm that finds success in many problem domains. The ecosystem of bat animals inspires the main idea of BA. This review paper scanned and analysed the state-of-the-art researches investigated using BA from 2017 to 2021. BA has very impressive characteristics such as its easy-to-use, simple in concepts, flexible and adaptable, consistent, and sound and complete. It has strong operators that incorporate the natural selection principle through survival-of-the-fittest rule within the intensification step attracted by local-best solution. Initially, the growth of the recent solid works published in Scopus indexed articles is summarized in terms of the number of BA-based Journal articles published per year, citations, top authors, work with BA, top institutions, and top countries. After that, the different versions of BA are highlighted to be in line with the complex nature of optimization problems such as binary, modified, hybridized, and multiobjective BA. The successful applications of BA are reviewed and summarized, such as electrical and power system, wireless and network system, environment and materials engineering, classification and clustering, structural and mechanical engineering, feature selection, image and signal processing, robotics, medical and healthcare, scheduling domain, and many others. The critical analysis of the limitations and shortcomings of BA is also mentioned. The open-source codes of BA code are given to build a wealthy BA review. Finally, the BA review is concluded, and the possible future directions for upcoming developments are suggested such as utilizing BA to serve in dynamic, robust, multiobjective, large-scaled optimization as well as improve BA performance by utilizing structure population, tuning parameters, memetic strategy, and selection mechanisms. The reader of this review will determine the best domains and applications used by BA and can justify their BA-related contributions.

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“…The paper "2D Tsallis entropy for image segmentation based on modified chaotic bat algorithm" by Ye et al [16] uses a modified chaotic bat algorithm (MCBA) to develop a 2D Tsallis entropy-based method for gray-level images segmentation. It employs MCBA to look for the best combination of all the parameters.…”

Section: Chaos-based Applicationsmentioning

confidence: 99%