Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding

Kurban, Tuba; Çivicioğlu, Pınar; Kurban, Rıfat; Beşdok, Erkan

doi:10.1016/j.asoc.2014.05.037

Cited by 120 publications

(50 citation statements)

References 49 publications

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“…However, in multi-level thresholding, the computational complexity grows exponentially [7]. Therefore, numerical evolutionary and swarm-based intellectual computation are introduced into MT [10].…”

Section: Related Workmentioning

confidence: 99%

“…It has been proved in the literature that intelligent optimizations are able to deliver better results than classical ones in terms of precision, processing speed, and robustness [5,10]. EMO was introduced for MT by Diego Olivaa et al [5], in which Kapur's entropy and Otsu's method are applied respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Such methods are time-consuming and computationally expensive. On the other hand, nonparametric methods try to determine the optimal thresholds by optimizing some standards, which include between-class variance, the entropy, the error rate, and so on [6][7][8][9][10][11]. The biggest advantages of such methods lie in their robustness and accuracy [5].…”

Section: Introductionmentioning

confidence: 99%

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A Quick Artificial Bee Colony Algorithm for Image Thresholding

Sun

Guo

et al. 2017

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Abstract:The computational complexity grows exponentially for multi-level thresholding (MT) with the increase of the number of thresholds. Taking Kapur's entropy as the optimized objective function, the paper puts forward the modified quick artificial bee colony algorithm (MQABC), which employs a new distance strategy for neighborhood searches. The experimental results show that MQABC can search out the optimal thresholds efficiently, precisely, and speedily, and the thresholds are very close to the results examined by exhaustive searches. In comparison to the EMO (Electro-Magnetism optimization), which is based on Kapur's entropy, the classical ABC algorithm, and MDGWO (modified discrete grey wolf optimizer) respectively, the experimental results demonstrate that MQABC has exciting advantages over the latter three in terms of the running time in image thesholding, while maintaining the efficient segmentation quality.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Quick Artificial Bee Colony Algorithm for Image Thresholding

Sun

Guo

et al. 2017

Information

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show abstract

“…In addition, their performance is highly dependent to some parameters which are usually set in a tedious trial-and error manner. More importantly, these methods cannot be robust because different results are obtained from different runs (Kurban et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Moghaddam

Mokhtarzade

Naeini

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Rational function models (RFMs) are known as one of the most appealing models which are extensively applied in geometric correction of satellite images and map production. Overfitting is a common issue, in the case of terrain dependent RFMs, that degrades the accuracy of RFMs-derived geospatial products. This issue, resulting from the high number of RFMs' parameters, leads to ill-posedness of the RFMs. To tackle this problem, in this study, a fast and robust statistical approach is proposed and compared to Tikhonov regularization (TR) method, as a frequently-used solution to RFMs' overfitting. In the proposed method, a statistical test, namely, significance test is applied to search for the RFMs' parameters that are resistant against overfitting issue. The performance of the proposed method was evaluated for two real data sets of Cartosat-1 satellite images. The obtained results demonstrate the efficiency of the proposed method in term of the achievable level of accuracy. This technique, indeed, shows an improvement of 50-80% over the TR.

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“…Authors [19] proposed Cuckoo Search algorithm (CS) and a nature inspired algorithm for the determination of optimal multilevel thresholding, exclusively for satellite image segmentation in view of Kapur's entropy. Authors [20] exhibited a point by point correlation of evolutionary and swam based computational strategies for optimal multilevel thresholding selection for color images taking into account Kapur's entropy. Kotte et.al presented PSNR maximization method for better multilevel thresholding image segmentation based on Improved Differential Search Algorithm (IDSA) [21].…”

Section: Introductionmentioning

confidence: 99%

Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm

Kotte¹,

Kumar²

2017

IJCA

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Image processing is one of the real research regions in the most recent four decades. Numerous researchers have contributed very great algorithms and reported outstanding results. In this paper, state of matter search optimization based multilevel thresholding is implemented for the segmentation of gray scale Images. Set of standard gray level images are considered for image segmentation. The optimal multilevel threshold is found by maximizing the very popular objectives such as between class variance (Otsu method) and Kapur's entropy. The outcomes are looked at with the aftereffects of the existing algorithms like IDSA, HSA, PSO, and BF. The outcomes uncover that the execution of state of matter search optimization algorithm based optimal multilevel threshold for image segmentation is better and has predictable execution than officially reported techniques. KeywordsMultilevel thresholding, gray scale image segmentation, state of matter search optimization, qualitative and quantitative analysis

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Comparison of evolutionary and swarm based computational techniques for multilevel color image thresholding

Cited by 120 publications

References 49 publications

A Quick Artificial Bee Colony Algorithm for Image Thresholding

A Quick Artificial Bee Colony Algorithm for Image Thresholding

Statistical Method to Overcome Overfitting Issue in Rational Function Models

Optimal Multilevel Threshold Selection for Gray Level Image Segmentation using SMS Algorithm

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