Image segmentation by multi-level thresholding based on fuzzy entropy and genetic algorithm in cloud

Muppidi, Mohan; Rad, Paul; Agaian, Sos S.; Jamshidi, Mo

doi:10.1109/sysose.2015.7151945

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…Although the method shows a comparative advantage over the Lopes method and Otsu thresholding, the experimental results are only applicable to bilevel thresholds and fail to show the effects of MT. Muppidi et al [31] used Triangular membership function, Trapezoidal membership function, and Bell-shaped membership function to define three kinds of fuzzy entropies, respectively, and search out the optimal parameters set through GA. Such methods are only compared with Otsu thresholding and failed to give adequate quantitative analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Option of membership function is the key factor for fuzzy thresholding segmentation. A large number of literature adopts different membership functions to test its effectiveness; trapezoidal membership function is the most commonly used and also the most efficient method [31]. Just as in literature [27], in the case that there are four fuzzy parameters, the curve of the fuzzy degrees can be shown in Figure 1.…”

Section: Concept Of Fuzzy Kapur's Entropy For Soft Thresholdingmentioning

confidence: 99%

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Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Sun

Guo

et al. 2016

Mathematical Problems in Engineering

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Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

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Section: Related Workmentioning

confidence: 99%

Section: Concept Of Fuzzy Kapur's Entropy For Soft Thresholdingmentioning

confidence: 99%

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Sun

Guo

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm gets better PSNR and Maximum Absolute Error comparatively than the region growing and OTSU [15]. Mohan Muppidi introduced a new soft computing method for segmentation of both intensity level and color images by using fuzzy entropy-based criteria(cost function) of the genetic algorithm, and the evolutionary computational techniques [16]. Jito Di Gesu and GL.…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation using Improved Genetic Algorithm

Kumari¹,

Gupta²,

Kumar³

2019

IJEAT

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Segmentation of image is a complex task. To recognize an image, segmentation is essential parts. During image segmentation, subsets of images on the basis of some features like gray levels values of pixels or position of pixels find out. This is an NP-complete problem, to find the solution to such problems is a computationally hard task. Some heuristic algorithm can be used to find out the solution to such a hard task. These algorithms find approximate solutions. Exact solution of such problems is not possible. Genetic algorithm can be considered a metaheuristic algorithm used the evolution of the population of solutions. This paper proposedan improved Genetic Algorithm that used to find multi-level thresholding segmentation of the image. The threshold value can be calculated by cumulative histogram and satisfactory result have been given by the experiments done on test images that are taken from Mnist datasets

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Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation

Yue

Hongbo

2019

Arab J Sci Eng

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Image segmentation by multi-level thresholding based on fuzzy entropy and genetic algorithm in cloud

Cited by 7 publications

References 20 publications

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Image Segmentation using Improved Genetic Algorithm

Improved Hybrid Bat Algorithm with Invasive Weed and Its Application in Image Segmentation

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