Estimation of Kapur Entropy Based Threshold Selection from Jaya Algorithm
Keywords:
Jaya algorithm, Kapur's Entropy, Multilevel thresholding, Soft computingAbstract
With a rapid expansion of image segmentation throughout the decades, the development of the scientific optimization as image segmentation is enormous on the segmentation. A need to organize the image thresholding arises to help medical imaging, detection, and recognition in making an informed decision about the image. Image segmentation dependent on computational intelligence approaches are utilized online to cluster the clinical imaging into positive or negative diagnosis. The proposed Jaya algorithm is relied upon to quickly get the top notch optimal thresholds are controlled by maximizing the Kapur entropy of various classes. Different from previous optimization techniques, Jaya algorithm has been utilized as a prime optimization method as it has been end up being a successful optimization when applied to different down to earth optimization issues and its execution is straightforward including less computational exertion. The technique has been tried on standard benchmark test images and the steady for all images even with the increase of the threshold. Numerical outcomes judgment shows that this algorithm is a promising choice for the multilevel image thresholding issue.References
Dey Sandip, Siddhartha Bhattacharyya, and Ujjwal Maulik. 2014. Quantum behaved multi-objective PSO and ACO optimization for multi-level thresholding. In Proceedings of the International Conference on Computational Intelligence and Communication Networks. IEEE Press, Bhopal. DOI: https://doi.org/10.1109/CICN.2014.63
Bhandari A. Kumar, Anil Kumar, and Girish K. Singh. 2015. Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms Expert Syst. Appl. 42, 22 (Dec. 2015), 8707-8730. DOI: https://doi.org/10.1016/j.eswa.2015.07.025
Otsu, Nobuyuki. 1979. A threshold selection method from gray-level histograms IEEE T. Syst. Man Cy-s 9, 1 (Jan. 1979), 62-66.
Kapur J. Narain, Prasanna K. Sahoo, and Andrew K.C. Wong. 1985. A new method for gray-level picture thresholding using the entropy of the histogram Comput. Gr.Image Process. 29, 3 (Mar. 1985), 273-285. DOI: https://doi.org/10.1016/0734-189X(85)90125-2
Li Kangshun and Zhiping Tan. 2019. An Improved Flower Pollination Optimizer Algorithm for Multilevel Image Thresholding IEEE Access 7 (Nov. 2019), 165571-165582. DOI: https://doi.org/10.1109/ACCESS.2019.2953494
Zarezadeh Somayeh and Majid Asadi. 2010. Results on residual Rényi entropy of order statistics and record values Inf. Sci. 180, 21 (Nov. 2010), 4195-4206. DOI: https://doi.org/10.1016/j.ins.2010.06.019
El Aziz, Mohamed Abd, Ahmed A. Ewees, and Aboul Ella Hassanien. 2017. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation Expert Syst. Appl. 83 (Oct. 2017), 242-256. DOI: https://doi.org/10.1016/j.eswa.2017.04.023
Abhay Sharma, Rekha Chaturvedi, Sandeep Kumar, and Umesh K. Dwivedi. 2020. Multi-level image thresholding based on Kapur and Tsallis entropy using firefly algorithm J. Interdiscip. Math. 23, 2, (Feb. 2020), 563-571. DOI: https://doi.org/10.1080/09720502.2020.1731976
Büşranur Küçükuğurlu and Gedikli Eyüp. 2020. Symbiotic Organisms Search Algorithm for multilevel thresholding of images Expert Syst. Appl. 147, (Jun. 2020), 113210. DOI: https://doi.org/10.1016/j.eswa.2020.113210
Venkata R. Rao. 2016. Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems Int. J. Ind. Eng. Comput. 7, 1, (2016), 19-34. DOI: https://doi.org/ 10.5267/j.ijiec.2015.8.004
Venkata R. Rao and K. C. More. 2017. Design optimization and analysis of selected thermal devices using self-adaptive Jaya algorithm Energy Convers. Manag. 140, (May 2017), 24-35. DOI: https://doi.org/10.1016/j.enconman.2017.02.068
Shuihua Wang, Venkata R. Rao, Chen Peng, Zhang Yudong, Liu Aijun, and Wei Ling. 2017. Abnormal breast detection in mammogram images by feed-forward neural network trained by Jaya algorithm Fundamenta Informaticae 151, 1-4, (Jan. 2017), 191-211. DOI: https://doi.org/10.3233/FI-2017-1487
Venkata R. Rao, Rai P. Dhiraj, and Balic Joze. 2016. Surface grinding process optimization using Jaya algorithm. In Proceedings of the Computational Intelligence in Data Mining Springer, New Delhi, 2, 487-495. DOI: https://doi.org/10.1007/978-81-322-2731-1_46
El Aziz, Mohamed Abd, Ahmed A. Ewees, and Aboul E. Hassanien. 2016. Hybrid swarms optimization based image segmentation. In Proceedings of the Hybrid Soft Computing for Image Segmentation. Springer, Cham, 1-21. DOI: https://doi.org/10.1007/978-3-319-47223-2_1
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