Multi-level image segmentation using randomized spiral-based whale optimization algorithm

被引：0

作者：

Shivahare B.D. ^{[1
]}

Gupta S.K. ^{[2
]}

机构：

[1] Department of Computer Science & Engg., A.K.T.U. Lucknow, Uttar Pradesh

[2] Department of Computer Science & Engg., B.I.E.T. Jhansi, Uttar Pradesh

来源：

Recent Patents on Engineering | 2021年 / 15卷 / 05期

关键词：

Berkeley; Image segmentation; Multi-level thresholding; Opti-mization; Swarm intelligence; Whale optimization algorithm;

D O I：

10.2174/1872212114999200730163151

中图分类号：

学科分类号：

摘要：

Background: Segmenting an image into multiple regions is a pre-processing phase of computer vision. For the same, determining an optimal set of thresholds is a challenging problem. Objective: This paper introduces a novel multi-level thresholding based image segmentation method. Methods: The presented method uses a novel variant of whale optimization algorithm to determine the optimal thresholds. For experimental validation of the proposed variant, twenty-three benchmark functions are considered. To analyze the efficacy of new multi-level image segmentation method, images from Berkeley Segmentation Dataset and Benchmark (BSDS300) have been considered and tested against recent multi-level image segmentation methods. Results: The segmentation results are validated in terms of subjective and objective evaluation. Conclusion: Experiments arm that the presented method is efficient and competitive than the exist-ing multi-level image segmentation methods. © 2021 Bentham Science Publishers.

引用

共 37 条

[1]

Jain A.K., Data clustering: 50 years beyond k-means, Pattern Recognit. Lett, 31, pp. 651-666, (2010)

[2]

Mittal H., Saraswat M., An image segmentation method using logarithmic kbest gravitational search algorithm based superpixel clustering, Evol. Intell, pp. 1-13, (2018)

[3]

Gervais H., Herumurti D., Computer-aided screening for acute leukemia blood infection using gray-level intensity, 2016 International Conference on Information & Communication Technology and Systems (ICTS), pp. 69-74, (2016)

[4]

Mittal H., Saraswat M., An optimum multi-level image thresholding segmentation using nonlocal means 2d histogram and exponential kbest gravitational search algorithm, Eng. Appl. Artif. Intell, 71, pp. 226-235, (2018)

[5]

Mittal H., Saraswat M., CKGSA based fuzzy clustering method for image segmentation of rgb-d images, Proc. of International Conference on Contemporary Computing, pp. 1-6, (2018)

[6]

Zhang Y.J., A survey on evaluation methods for image segmenta-tion, Pattern Recognit, 29, 8, pp. 1335-1346, (1996)

[7]

Mittal H., Saraswat M., Classification of histopathological images through bag-of-visual-words and gravitational search algo-rithm, Lect. Notes of Soft Computing for Problem Solving, pp. 231-241, (2019)

[8]

Tripathi A. K., Sharma K., Bala M., A novel clustering method using enhanced grey wolf optimizer and mapreduce, Big Data Research, 14, pp. 93-100, (2018)

[9]

Gupta V., Singh A., Sharma K., Mittal H., A novel deerential evolution test case optimisation (detco) technique for branch cover-age fault detection, Smart Computing and Informatics, pp. 245-254, (2018)

[10]

Shahri E.S.A., Alfi A., Machado J.T., Fractional fixed-structure h1 controller design using augmented lagrangian particle swarm optimization with fractional order velocity, Appl. Soft Comput, 77, pp. 688-695, (2019)

← 1 2 3 4 →